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  • 1
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 8 (1997), S. 227-234 
    ISSN: 1572-8145
    Schlagwort(e): Proportional hazards models ; neural networks ; accelerated life testing
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract Because of increased manufacturing competitiveness, new methods for reliability estimation are being developed. Intelligent manufacturing relies upon accurate component and product reliability estimates for determining warranty costs, as well as optimal maintenance, inspection, and replacement schedules. Accelerated life testing is one approach that is used for shortening the life of products or components or hastening their performance degradation with the purpose of obtaining data that may be used to predict device life or performance under normal operating conditions. The proportional hazards (PH) model is a non-parametric multiple regression approach for reliability estimation, in which a baseline hazard function is modified multiplicatively by covariates (i.e. applied stresses). While the PH model is a distribution-free approach, specific assumptions need to be made about the time behavior of the hazard rates. A neural network (NN) is particularly useful in pattern recognition problems that involve capturing and learning complex underlying (but consistent) trends in the data. Neural networks are highly non-linear, and in some cases are capable of producing better approximations than multiple regression. This paper reports on the comparison of PH and NN models for the analysis of time-dependent dielectric breakdown data for a metal-oxide-semiconductor integrated circuit. In this case, the NN model results in a better fit to the data based upon minimizing the mean square error of the predictions when using failure data from an elevated temperature and voltage to predict reliability at a lower temperature and voltage.
    Materialart: Digitale Medien
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  • 2
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 7 (1996), S. 1-11 
    ISSN: 1572-8145
    Schlagwort(e): Artificial intelligence ; computer vision ; neural networks ; shape recognition ; structural model
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Shape representation and recognition is an important topic in many applications of computer vision and artificial intelligence, including character recognition, pattern recognition, machine monitoring, robot manipulation and production part recognition. In this paper, a structural model based on boundary information is proposed to describe the silhouette of planar objects (especially machined parts). The structural model describes objects by a set of primitives, each of which is represented by three geometric features: its length, curvature, and relative orientation. This representation scheme not only compresses the data, but also provides a compact and meaningful form to facilitate further recognition operations. Based on this model, the object recognition is accomplished by using a multilayered feedforward neural network. The proposed model is transformation invariant, which offers the necessary flexibility for real-time implementation in automated manufacturing systems. In addition, the numerical results for a set of ten reference shapes indicate that the matching engine can achieve very high success rates using short recognition times.
    Materialart: Digitale Medien
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  • 3
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 9 (1998), S. 347-352 
    ISSN: 1572-8145
    Schlagwort(e): Sheet metal parts ; computer-aided process planning ; bending tools ; laminated object modelling ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract The manufacturing of complex bent parts can be supported effectively by computer-aided planning methods. Software systems are already available for unfolding, laser cutting and bending sequence determination. The paper focuses on methods that support the design of non-standard bending tools and the flexible manufacturing of such tools using laminated object modelling (LOM) technology. The developed system allows for concurrent planning and manufacturing of bending parts and tools. Within the framework of this system, neural networks are applied for automated tool design.
    Materialart: Digitale Medien
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  • 4
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 9 (1998), S. 281-287 
    ISSN: 1572-8145
    Schlagwort(e): Turning ; accuracy ; process control ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract Stochastic and non-deterministic influences have an effect on cutting processes and lead to an unsteady and dynamic process behaviour. Concepts for the improvement of process reliability and for the control of tolerances have to be developed in order to fulfil the increasing requirements on product quality. A concept for the improvement of manufacturing accuracy through artificial neural networks (ANN) will be presented as an example for the turning process. This ANN model makes it possible to predict the dimensional deviation caused by tool wear. Feeding this back in an open loop within the machine controller the deviation can be compensated by using an adaptive control of the depth of cut.
    Materialart: Digitale Medien
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  • 5
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 8 (1997), S. 203-214 
    ISSN: 1572-8145
    Schlagwort(e): Feature recognition ; feature representation ; neural networks ; ART
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract A self-organizing neural network, ART2, based on adaptive resonance theory (ART), is applied to the problem of feature recognition from a boundary representation (B-rep) solid model. A modified face score vector calculation scheme is adopted to represent the features by continuous-valued vectors, suitable to be input to the network. The face score is a measure of the face complexity based upon the convexity or concavity of the surrounding region. The face score vector depicts the topological relations between a face and its neighbouring faces. The ART2 network clusters similar features together. The similarity of the features within a cluster is controlled by a vigilance parameter. A new feature presented to the net is associated with one of the existing clusters, if the feature is similar to the members of the cluster. Otherwise, the net creates a new cluster. An algorithm of the ART2 network is implemented and tested with nine different features. The results obtained indicate that the network has significant potential for application to the problem of feature recognition.
    Materialart: Digitale Medien
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  • 6
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 10 (1999), S. 251-265 
    ISSN: 1572-8145
    Schlagwort(e): Feature-based design for manufacture ; feature recognition ; feature families formation ; cell grouping ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract A neural network approach is applied to the problem of integrating design and manufacturing engineering. The self organising map (SOM) neural network recognizes products and parts which are modeled as boundary representation (B-rep) solids using a modified face complexity code scheme adopted, and forms the necessary feature families. Based on the part features, machines, tools and fixtures are selected. These information are then fed into a four layer feed-forward neural network that provides a designer with the desired features that meet the current manufacturing constraints for design of a new product or part. The proposed methodology does not involve training of the neural networks used and is seen to be a significant potential for application in concurrent engineering where design and manufacturing are integrated.
    Materialart: Digitale Medien
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  • 7
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 10 (1999), S. 289-299 
    ISSN: 1572-8145
    Schlagwort(e): Feature recognition ; feature representation ; neural networks ; BPN
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract A backpropagation neural network (BPN) is applied to the problem of feature recognition from a boundary representation (B-rep) solid model to facilitate process planning of manufactured products. It is based on the use of the face complexity code to represent the features and a neural network for the analysis of the recognition. The face complexity code is a measure of the face complexity of a feature based on the convexity or concavity of the surrounding geometry. The codes for various features are fed to the network for analysis. A backpropagation network is implemented for recognition of features and tested on published results to measure its performance. Any two or more features having significant differences in face complexity codes were used as exemplars for training the network. A new feature presented to the network is associated with one of the existing clusters, if they are similar, or the network creates a new cluster, if otherwise. Experimental results show that the network was consistent in recognizing features, hence is appropriate for application to the problem of feature recognition in automated manufacturing environment.
    Materialart: Digitale Medien
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  • 8
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 10 (1999), S. 405-421 
    ISSN: 1572-8145
    Schlagwort(e): Flexible manufacturing systems control ; intelligent manufacturing ; neural networks ; simulation ; material handling systems ; automated guided vehicles
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract This paper presents a framework of intelligent manufacturing scheduling and control with specific applications to operations of rail-guided vehicle systems (RGVS). A RGVS control architecture is discussed with a focus on a simulated experiment in operations of the load/unload area of a real industrial flexible manufacturing system (FMS). In the operation stage of a material handling system (MHS), all shop floor data are subject to change as time goes. These data can be collected using a data acquisition device and stored in a dynamic database. The RGVS simulator used in this experimental study is designed to incorporate some possible situations representing existing material handling scenarios in order to evaluate alternative control policies. At the development stage of the controller, all possible combinations of most commonly encountered scenarios such as RGV failures, production schedule changes, machine breakdowns, and rush orders are to be simulated and corresponding results collected. The data are then structured into training data pairs to properly train an artificial neural network. The neural network, trained by using input/output data sets obtained from a number of simulation runs, will then provide control strategy recommendations. At the application stage, whenever an abnormal scenario occurs, a pre-processor will be activated to pre-screen and prepare an input vector for the trained neural network. If such an abnormal scenario falls outside the existing domain of data sets employed to train the neural network, as judged by the MHS supervisory controller, an off-line training module will be activated to eventually update the neural network. The recommended control strategies will be transmitted to the MHS control for real-time execution. If there is no further abnormal event detected, the dynamic data base (DDB) module simply continues to monitor the MHS activities. The proposed MHS control system combines the features of example based neural network technology and simulation modeling for true intelligent, on-line, pseudo real-time control. Not only will the system assure that feasible material handling control actions be taken, but also it will implement better control decisions through continuous learning from experiences captured as the operation time of the MHS accumulates.
    Materialart: Digitale Medien
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  • 9
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 9 (1998), S. 289-294 
    ISSN: 1572-8145
    Schlagwort(e): Manufacturing process chain ; modelling ; optimization ; neural networks ; evolutionary algorithms
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract Today's manufacturing methods are caught between the growing need for quality, high process safety, minimal manufacturing costs, and short manufacturing times. In order to meet these demands, process setting parameters have to be chosen in the best possible way, according to demand on quality. For such optimization it is necessary to represent the processes in a model. Due to the enormous complexity of many processes and the high number of influencing parameters, however, conventional approaches to modelling and optimization are no longer sufficient. In this article it is shown how, by means of applying neural networks for process modelling, even these highly complex interdependencies can be learned. That way both process and quality parameters can be assessed before or during processing. By connecting them with corresponding cost models, it is possible to optimize processes with the help of evolutionary algorithms. Using examples of different manufacturing processes, the possi bilities for process modelling and optimization with neural networks and evolutionary algorithms are demonstrated.
    Materialart: Digitale Medien
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  • 10
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 8 (1997), S. 177-190 
    ISSN: 1572-8145
    Schlagwort(e): Nesting ; stock cutting ; neural networks ; optimization ; genetic algorithms
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract In this study, two approaches are explored for the solution of the rectangular stock cutting problem: neuro-optimization, which integrates artificial neural networks and optimization methods; and genetic neuro-nesting, which combines artificial neural networks and genetic algorithms. In the first approach, an artificial neural network architecture is used to generate rectangular pattern configurations, to be used by the optimization model, with an acceptable scrap. Rectangular patterns of different sizes are selected as input to the network to generate the location and rotation of each pattern after they are combined. A mathematical programming model is used to determine the nesting of different sizes of rectangular patterns to meet the demand for rectangular blanks for a given planning horizon. The test data used in this study is generated randomly from a specific normal distribution. The average scrap percentage obtained is within acceptable limits. In the second approach, a genetic algorithm is used to generate sequences of the input patterns to be allocated on a finite width with infinite-length material. Each gene represents the sequence in which the patterns are to be allocated using the allocation algorithm developed. The scrap percentage of each allocation is used as an evaluation criterion for each gene for determining the best allocation while considering successive generations. The allocation algorithm uses the sliding method integrated with an artificial neural network based on the adaptive resonance theory (ART1) paradigm to allocate the patterns according to the sequence generated by the genetic algorithm. It slides an incoming pattern next to the allocated ones and keeps all scrap areas produced, which can be utilized in allocating a new pattern through the ART1 network. If there is a possible match with an incoming pattern and one of the scrap areas, the neural network selects the best match area and assigns the pattern. Both approaches gave satisfactory results. The second approach generated nests having packing densities in the range 95–97%. Improvement in packing densities was possible at the expense of excessive computational time. Parallel implementation of this unconventional approach could well bring a quick and satisfactory solution to this classical problem.
    Materialart: Digitale Medien
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  • 11
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 9 (1998), S. 353-359 
    ISSN: 1572-8145
    Schlagwort(e): Cold forging ; process planning ; fuzzy logic ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract In this paper, two artificial intelligence (AI) techniques were applied to the problem of process planning in multiple-blow cold forging. Given the reduction in area of the product to be forged and the degree of formability of the material, in the first application a fuzzy logic (FL) technique was used to discriminate whether or not a cold forged product was feasible in a single blow. In the second application, a neural network (NN) architecture was used to identify the correct number of blows necessary to complete the cold forging process.
    Materialart: Digitale Medien
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  • 12
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 6 (1995), S. 377-387 
    ISSN: 1572-8145
    Schlagwort(e): Job-shop scheduling ; neural networks ; constraint satisfaction ; optimization ; rules of thumb
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Recently neural network architectures have been developed that are capable of solving deterministic job-shop scheduling problems, part of the large class of NP-complete problems. In these architectures, however, no valid optimization criterion has been implemented. In this paper an enhanced neural network architecture for job-shop scheduling is proposed in which general rules of thumb for job-shop scheduling have been incorporated as a local optimization criterion. Implementation of the rules of thumb, by adaptation of the network architecture, results in a network that actually incorporates the optimization criterion, enabling parallel hardware implementation. Owing to the implemented local optimization criterion the performance of the network architecture is superior to previously presented architectures. Comparison with advanced heuristic sequential schedulers showed equal performance with respect to the quality of the solutions and better performance with respect to calculation speed.
    Materialart: Digitale Medien
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  • 13
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 7 (1996), S. 23-38 
    ISSN: 1572-8145
    Schlagwort(e): Intelligent process control ; molding ; neural networks ; nonlinear optimization ; Taguchi method
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: current investigation focused on neural-network-based control of manufacturing processes utilizing an optimization scheme. In an earlier study, Demirci and Coulter introduced the utilization of neural networks for the intelligent control of molding processes. In that study, a forward model neural network, employed with a search strategy based on the factorial design of experiments method, was shown to successfully control the flow progression during injection molding processes. Recently, Demirciet al. showed that the search mechanism based on the factorial design of experiments method can be intolerable in time during on-line control of manufacturing processes, and suggested an inverse model neural network. This inverse model neural network was shown to be beneficial as it totally eliminated time-consuming parameter searches, but it required a harder mapping than the forward model neural network and thus its performance was inferior. In the present study, the authors investigated two different optimization methods that were utilized in making the search method of the forward control scheme more efficient. The first method was Taguchi's method of parameter design, and the second method was a nonlinear optimization method known as Nelder and Mead's downhill simplex method. These two methods were separately utilized in creating an efficient search method to be used with the forward model neural network. The performance of the resulting two control methods was compared with each other as well as with that of the forward control scheme utilizing a search strategy based on the factorial design of experiments method. Although the applications in this study were on molding processes, the method can be applied to any manufacturing process for which a process model and anin-situ sensing scheme exists.
    Materialart: Digitale Medien
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  • 14
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 7 (1996), S. 243-250 
    ISSN: 1572-8145
    Schlagwort(e): Job-shop scheduling ; real-time dispatching ; simulation ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: To schedule a job shop, the first task is to select an appropriate scheduling algorithm or rule. Because of the complexity of scheduling problems, no general algorithm sufficient for solving all scheduling problems has yet been developed. Most job-shop scheduling systems offer alternative algorithms for different situations, and experienced human schedulers are needed to select the best dispatching rule in these systems. This paper proposes a new algorithm for job-shop scheduling problems. This algorithm consists of three stages. First, computer simulation techniques are used to evaluate the efficiency of heuristic rules in different scheduling situations. Second, the simulation results are used to train a neural network in order to capture the knowledge which can be used to select the most efficient heuristic rule for each scheduling situation. Finally, the trained neural network is used as a dispatching rule selector in the real-time scheduling process. Research results have shown great potential in using a neural network to replace human schedulers in selecting an appropriate approach for real-time scheduling. This research is part of an ongoing project of developing a real-time planning and scheduling system.
    Materialart: Digitale Medien
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  • 15
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 7 (1996), S. 329-339 
    ISSN: 1572-8145
    Schlagwort(e): Automated tuning ; electronic circuit ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Manufacturing of electronic circuits for microwave communication boards often requires tuning of different circuit characteristics by manual adjustment of several trimmer components, including the trimmer's resistance and capacitance. This manual tuning process was automated by applying the artificial neural network modeling approach. In the considered tuning process, which required manual adjustment of a set of trimmers, multiple specification criteria had to be satisfied by several trimmer rotations. The tuning process was described in terms of three independent steps: the circuit output measurement, trimmer selection, and trimmer rotation. The trimmer selection was performed by a semi-supervised neural network, which learned the patterns of circuit characteristics and the deviations between the ideal and practical outputs. Another network was developed for determination of trimmer rotation rate. The results, based on computer simulation of the tuning process, showed that the developed system improved performance of the tuning process, allowing for automation of the microwave circuit board tuning task in a real manufacturing environment.
    Materialart: Digitale Medien
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  • 16
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 9 (1998), S. 303-314 
    ISSN: 1572-8145
    Schlagwort(e): Grinding modelling ; neural networks ; fuzzy set theory ; genetic algorithm ; grinding information system
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract The paper describes different methods for modelling and optimization of grinding processes. First the process and product quality characterizing quantities have to be measured. Afterwards different model types, e.g. physical–empirical basic grinding models as well as empirical process models based on neural networks, fuzzy set theory and standard multiple regression methods, are discussed for an off-line process conceptualization and optimization using a genetic algorithm. The assessment of grinding process results, which build the individuals in the genetic algorithm's population, is carried out using a target tree method. The methods presented are integrated into an existing grinding information system, which is part of a three control loop system for quality assurance.
    Materialart: Digitale Medien
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  • 17
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 6 (1995), S. 175-190 
    ISSN: 1572-8145
    Schlagwort(e): Concurrent engineering ; cell design ; cell control ; simulation ; knowledge-based expert system ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract One of the major thrusts of ‘agile/lean/responsive’ manufacturing strategies of the twentyfirst century is to introduce advanced information technology into manufacturing. This paper presents a framework for robust manufacturing system design with the integration of simulation, neural networks and knowledge-based expert system tools. An operation/ cost-driven cell design methodology was applied to concurrently consider cell physical design and the complexity of cell control functions. Simulation was exercised to estimate performance measures based on input parameters and given cell configurations. A rulebased expert system was employed to store the acquired expert knowledge regarding the relation between cell control complexities, cost of cell controls, performance measures and cell configuration. Neural networks were applied to predict the cell design configuration and corresponding complexities of cell control functions. Training of neural networks was performed with both forward and backward methods by using the same pair of data sets. Hence, trained neural networks will be able to predict either input or output parameters. This innovative new design methodology was illustrated via a successful implementation exercise resulting in actually acquiring an automated cell at industrial settings. The experience learned from this exercise indicates that the proposed design methodology works well as an effective decision support system for cell designers and the management in determining appropriate cell configuration and cell control functions at the design stage.
    Materialart: Digitale Medien
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  • 18
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 8 (1997), S. 167-175 
    ISSN: 1572-8145
    Schlagwort(e): Pattern recognition ; neural networks ; time series ; feature extraction
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract This paper presents a new approach for automated parts recognition. It is based on the use of the signature and autocorrelation functions for feature extraction and a neural network for the analysis of recognition. The signature represents the shapes of boundaries detected in digitized binary images of the parts. The autocorrelation coefficients computed from the signature are invariant to transformations such as scaling, translation and rotation of the parts. These unique extracted features are fed to the neural network. A multilayer perceptron with two hidden layers, along with a backpropagation learning algorithm, is used as a pattern classifier. In addition, the position information of the part for a robot with a vision system is described to permit grasping and pick-up. Experimental results indicate that the proposed approach is appropriate for the accurate and fast recognition and inspection of parts in automated manufacturing systems.
    Materialart: Digitale Medien
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  • 19
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent manufacturing 9 (1998), S. 447-455 
    ISSN: 1572-8145
    Schlagwort(e): Cutting ; tool condition monitoring ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract Cutting forces and acoustic emission measures as a function of tool wear are presented for different cutting parameters and their applicability for tool condition monitoring is evaluated. The best of them, together with cutting parameters, were chosen as inputs to a feedforward, back propagation (FFBP) neural network; some training techniques were applied and their effectiveness is also evaluated. Conventional training of FFBP neural networks very soon leads to overtraining, hence to deterioration in the net response. Training of these nets depends very much on the initial weight values. A good way of finding satisfactory results is to introduce random distortions to the weight system, which efficiently push the net out of a local minimum of testing errors. An even more effective method may be to employ temporary shifts in the weights, alternately negative and positive. This has two advantages: (1) it brings the net to balance between training and testing errors and (2) it enables a great reduction in the number of hidden nodes.
    Materialart: Digitale Medien
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  • 20
    Digitale Medien
    Digitale Medien
    Springer
    International journal of flexible manufacturing systems 7 (1995), S. 147-175 
    ISSN: 1572-9370
    Schlagwort(e): inductive learning ; intelligent scheduling ; neural networks ; FMS
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract With the growing uncertainty and complexity in the manufacturing environment, most scheduling problems have been proven to be NP-complete and this can degrade the performance of conventional operations research (OR) techniques. This article presents a system-attribute-oriented knowledge-based scheduling system (SAOSS) with inductive learning capability. With the rich heritage from artificial intelligence (AI), SAOSS takes a multialgorithm paradigm which makes it more intelligent, flexible, and suitable than others for tackling complicated, dynamic scheduling problems. SAOSS employs an efficient and effective inductive learning method, a continuous iterative dichotomister 3 (CID3) algorithm, to induce decision rules for scheduling by converting corresponding decision trees into hidden layers of a self-generated neural network. Connection weights between hidden units imply the scheduling heuristics, which are then formulated into scheduling rules. An FMS scheduling problem is also given for illustration. The scheduling results show that the system-attribute-oriented knowledge-based approach is capable of addressing dynamic scheduling problems.
    Materialart: Digitale Medien
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  • 21
    Digitale Medien
    Digitale Medien
    Springer
    Artificial intelligence and law 3 (1995), S. 267-275 
    ISSN: 1572-8382
    Schlagwort(e): Legal Expert Systems ; sequenced transition networks ; neural networks ; ID3 algorithm ; Toulmin Argument Structures ; case-based reasoning ; production rule expert system ; divorce ; property division ; explanation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Rechtswissenschaft
    Materialart: Digitale Medien
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  • 22
    Digitale Medien
    Digitale Medien
    Springer
    Artificial intelligence and law 7 (1999), S. 115-128 
    ISSN: 1572-8382
    Schlagwort(e): analogy ; fuzzy logic ; learning ; legal formalism ; neural networks ; vagueness
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Rechtswissenschaft
    Notizen: Abstract Computational approaches to the law have frequently been characterized as being formalistic implementations of the syllogistic model of legal cognition: using insufficient or contradictory data, making analogies, learning through examples and experiences, applying vague and imprecise standards. We argue that, on the contrary, studies on neural networks and fuzzy reasoning show how AI & law research can go beyond syllogism, and, in doing that, can provide substantial contributions to the law.
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  • 23
    Digitale Medien
    Digitale Medien
    Springer
    Artificial intelligence and law 7 (1999), S. 129-151 
    ISSN: 1572-8382
    Schlagwort(e): connectionism ; legal philosophy ; legal theory ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Rechtswissenschaft
    Notizen: Abstract This paper examines the use of connectionism (neural networks) in modelling legal reasoning. I discuss how the implementations of neural networks have failed to account for legal theoretical perspectives on adjudication. I criticise the use of neural networks in law, not because connectionism is inherently unsuitable in law, but rather because it has been done so poorly to date. The paper reviews a number of legal theories which provide a grounding for the use of neural networks in law. It then examines some implementations undertaken in law and criticises their legal theoretical naïvete. It then presents a lessons from the implementations which researchers must bear in mind if they wish to build neural networks which are justified by legal theories.
    Materialart: Digitale Medien
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  • 24
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 23 (1998), S. 105-128 
    ISSN: 1573-0409
    Schlagwort(e): neural networks ; robust control ; back stepping control ; adaptive control
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract Neural network (NN) controllers for the robust back stepping control of robotic systems in both continuous and discrete-time are presented. Control action is employed to achieve tracking performance for unknown nonlinear system. Tuning methods are derived for the NN based on delta rule. Novel weight tuning algorithms for the NN are obtained that are similar to ε-modification in the case of continuous-time adaptive control. Uniform ultimate boundedness of the tracking error and the weight estimates are presented without using the persistency of excitation (PE) condition. Certainty equivalence is not used and regression matrix is not computed. No learning phase is needed for the NN and initialization of the network weights is straightforward. Simulation results justify the theoretical conclusions.
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  • 25
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 25 (1999), S. 121-132 
    ISSN: 1573-0409
    Schlagwort(e): invariant object recognition ; pattern recognition ; neural networks ; flexible manufacturing
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract A small flexible production cell has been built around a selectively compliant articulated robot arm. Moving on a conveyor belt, boxes marked with different labels are presented to the robot in a random order. Using a camera and a vision card, the labels on the boxes are recognized. Each one of the labels can be rotated, translated or scaled. Three different invariant feature extraction methods (signature, invariant moments of Hu and Zernike) are compared. A neural net is used to classify the labels. The task of the SCARA robot is to pick up the moving boxes and to sort them according to their labels.
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  • 26
    Digitale Medien
    Digitale Medien
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    Journal of intelligent and robotic systems 16 (1996), S. 229-243 
    ISSN: 1573-0409
    Schlagwort(e): Redundant robots ; obstacle avoidance ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract A computationally efficient, obstacle avoidance algorithm for redundant robots is presented in this paper. This algorithm incorporates the neural networks and pseudodistance function D p in the framework of resolved motion rate control. Thus, it is well suited for real-time implementation. Robot arm kinematic control is carried out by the Hopfield network. The connection weights of the network can be determined from the current value of Jacobian matrix at each sampling time, and joint velocity commands can be generated from the outputs of the network. The obstacle avoidance task is achieved by formulating the performance criterion as D p〉d min (d min represents the minimal distance between the redundant robot and obstacles). Its calculation is only related to some vertices which are used to model the robot and obstacles, and the computational times are nearly linear in the total number of vertices. Several simulation cases for a four-link planar manipulator are given to prove that the proposed collision-free trajectory planning scheme is efficient and practical.
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  • 27
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 17 (1996), S. 283-308 
    ISSN: 1573-0409
    Schlagwort(e): manufacturing ; motion in contact ; force/torque sensors ; error detection ; plan monitoring ; uncertainty ; robotics ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract This paper presents a new approach to error detection during motion in contact under uncertainty for robotic manufacturing tasks. In this approach, artificial neural networks are used for perception-based learning. The six force-and-torque signals from the wrist sensor of a robot arm are fed into the network. A self-organizing map is what learns the different contact states in an unsupervised way. The method is intended to work properly in complex real-world manufacturing environments, for which existent approaches based on geometric analytical models may not be feasible, or may be too difficult. It is used for different tasks involving motion in contact, particularly the peg-in-hole insertion task, and complex insertion or extraction operations in a flexible manufacturing system. Several real examples for these cases are presented.
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  • 28
    Digitale Medien
    Digitale Medien
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    Journal of intelligent and robotic systems 17 (1996), S. 327-349 
    ISSN: 1573-0409
    Schlagwort(e): neural networks ; flexible-link robots ; singular perturbation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract The object in this paper is to achieve tracking control of a partially unknown flexible-link robot arm. It is shown how to stabilize the internal dynamics by selecting a physically meaningful modified performance output for tracking; this output is the slow portion of the link-tip motions. That is, the tracking requirement is relaxed so that the internal dynamics are controllable through a boundary layer correction. The controller is composed of singular-perturbation based fast control and an outer-loop slow control. The slow subsystem is controlled by a neural network (NN) for feedback linearization, plus a PD outer-loop for tracking, and a robustifying term to assure the closed-loop stability. No off-line learning or training is needed for the NN. Tracking and stability are proven using Lyapunov techniques that yield a novel modified NN weight tuning algorithm.
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  • 29
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 22 (1998), S. 351-374 
    ISSN: 1573-0409
    Schlagwort(e): micromanipulation station ; autonomous robots ; microrobots ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract Micromanipulation has become an issue of primary importance in industry and biomedicine, since human manual capabilities are restricted to certain tolerances. The manipulation of biological cells or the assembly of a complete microsystem composed of different microcomponents are examples of the application of piezoelectric-driven microrobots. An automated microrobot-based micromanipulation desktop-station is developed by an interdisciplinary group at the University of Karlsruhe. The process of assembly takes place in the field of view of a light optical microscope. This paper focuses on motion control problems of the microrobots. The ability of an intelligent microsystem to adapt itself to the process requirements is of great importance, especially for assembly robots. The microrobots must be able to operate in a partially defined environment and to ensure reasonable behaviour in unpredicted situations. A neural control concept based on a reference model approach is proposed as a solution. It is shown, that the neural controller is able to learn the desired behaviour. It considerably outperforms an analytically designed linear controller. This is demonstrated both in simulation and in the real environment.
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  • 30
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 24 (1999), S. 43-68 
    ISSN: 1573-0409
    Schlagwort(e): learning robots ; system organization ; optimization ; physical equation ; look-ut table ; neural networks ; fuzzy controllers
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract This paper explores a stepwise learning approach based on a system's decomposition into functional subsystems. Two case studies are examined: a visually guided robot that learns to track a maneuvering object, and a robot that learns to use the information from a force sensor in order to put a peg into a hole. These two applications show the features and advantages of the proposed approach: i) the subsystems naturally arise as functional components of the hardware and software; ii) these subsystems are building blocks of the robot behavior and can be combined in several ways for performing various tasks; iii) this decomposition makes it easier to check the performances and detect the cause of a malfunction; iv) only those subsystems for which a satisfactory solution is not available need to be learned; v) the strategy proposed for coordinating the optimization of all subsystems ensures an improvement at the task-level; vi) the overall system's behavior is significantly improved by the stepwise learning approach.
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  • 31
    Digitale Medien
    Digitale Medien
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    Journal of intelligent and robotic systems 22 (1998), S. 255-267 
    ISSN: 1573-0409
    Schlagwort(e): localisation ; neural networks ; stochastic diffusion
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract This paper introduces the Focused Stochastic Diffusion Network as a novel method of self-localisation for an autonomous wheelchair in a complex, busy environment. The space of possible positions is explored in parallel by a set of cells searching in a competitive co-operative manner for the most likely position of the wheelchair in its environment. Experimental results from the SENARIO autonomous wheelchair project indicate the technique is practical and robust.
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  • 32
    Digitale Medien
    Digitale Medien
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    Journal of intelligent and robotic systems 17 (1996), S. 81-99 
    ISSN: 1573-0409
    Schlagwort(e): forging automation ; compliance control ; neural networks ; dynamic simulation ; finite element models ; recursive algorithms ; intelligent manufacturing
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract Automation of forging processes is important for both safety and efficiency in today's advanced manufacturing operations. This work supports the development of an Intelligent Open Die Forging System which will integrate state-of-the-art modelling techniques, automatic die selection and sequencing, full system dynamic simulation, automatic machine programming and coordination, and sensor-based process control to enable the production of more general and complex workpiece geometries than are achievable using current forging methods. Effective automation of this open die forging system requires the coordination and control of the major system components: press, robot, and furnace. In particular, forces exerted on the robot through its manipulation of the workpiece during forging must be minimized to avoid damage to the manipulator mechanism. In this paper, the application of neural networks for compliance control of the forging robot to minimize these forces is investigated. Effectiveness of the neural network-based compliance control module is evaluated through a full dynamic system simulation, which will later form a central part of the complete Intelligent Forging System. Dynamic simulation of the robot is achieved using an efficient O(N) recursive algorithm, while material flow of the workpiece is modeled with a finite element approach. Simulation and timing results for the complete processing system for a specific open die forging example are presented.
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  • 33
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 20 (1997), S. 295-317 
    ISSN: 1573-0409
    Schlagwort(e): nonholonomic systems ; mobile robots ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract A control structure that makes possible the integration of a kinematiccontroller and a neural network (NN) computed-torque controller fornonholonomic mobile robots is presented. A combined kinematic/torque controllaw is developed and stability is guaranteed by Lyapunov theory. Thiscontrol algorithm is applied to the practical point stabilization problemi.e., stabilization to a small neighborhood of the origin. The NN controllercan deal with unmodeled bounded disturbances and/or unstructured unmodeleddynamics in the vehicle. On-line NN weight tuning algorithms that do notrequire off-line learning yet guarantee small tracking errors and boundedcontrol signals are utilized.
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  • 34
    Digitale Medien
    Digitale Medien
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    Journal of intelligent and robotic systems 20 (1997), S. 181-193 
    ISSN: 1573-0409
    Schlagwort(e): robot manipulators ; adaptive control ; neural networks ; stability analysis
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract In this paper a controller based on neural networks is proposed toachieve output trajectory tracking of rigid robot manipulators. Neuralnetworks used here are one hidden layer ones so that their outputs dependlinearly on the parameters. Our method uses a decomposed connectioniststructure. Each neural network approximate a separate element of thedynamical model. These approximations are used to perform an adaptive stablecontrol law. The controller is based on direct adaptive techniques and theLyapunov approach is used to derive the adaptation laws of the nets’parameters. By using an intrinsic physical property of the manipulator, thesystem is proved to be stable. The performance of the controller depends onthe quality of the approximation, i.e. on the inherent reconstruction errorsof the exact functions.
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  • 35
    Digitale Medien
    Digitale Medien
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    Journal of intelligent and robotic systems 21 (1998), S. 143-154 
    ISSN: 1573-0409
    Schlagwort(e): neural networks ; sensor calibration ; fault detection and isolation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract Recently, the use of Autoassociative Neural Networks (AANNs) to perform on-line calibration monitoring of process sensors has been shown to not only be feasible, but practical as well. This paper summarizes the results of applying AANNs to instrument surveillance and calibration monitoring at Florida Power Corporation's Crystal River #3 Nuclear Power Plant and at the Oak Ridge National Laboratory High Flux Isotope Reactor. In both cases sensor drifts are detectable at a nominal level of 0.5% instrument's full scale range. This paper will discuss the selection of a five layer neural network architecture, a robust training paradigm, the input selection criteria, and a retuning algorithm.
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  • 36
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 26 (1999), S. 91-100 
    ISSN: 1573-0409
    Schlagwort(e): robots ; neural networks ; adaptiveness ; stability ; approximation
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract An indirect adaptive control approach is developed in this paper for robots with unknown nonlinear dynamics using neural networks (NNs). A key property of the proposed approach is that the actual joint angle values in the control law are replaced by the desired joint angles, angle velocities and accelerators, and the bound on the NN reconstruction errors is assumed to be unknown. Main theoretical results for designing such a neuro-controller are given, and the control performance of the proposed controller is verified with simulation studies.
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  • 37
    Digitale Medien
    Digitale Medien
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    Journal of intelligent and robotic systems 20 (1997), S. 251-273 
    ISSN: 1573-0409
    Schlagwort(e): robot control ; adaptive behavior ; robust intelligent control ; multi-robot systems ; machine learning ; neural networks ; genetic algorithms ; cognitive architecture.
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract The objective of this paper is to present a cognitive architecture thatutilizes three different methodologies for adaptive, robust control ofrobots behaving intelligently in a team. The robots interact within a worldof objects, and obstacles, performing tasks robustly, while improving theirperformance through learning. The adaptive control of the robots has beenachieved by a novel control system. The Tropism-based cognitive architecturefor the individual behavior of robots in a colony is demonstrated throughexperimental investigation of the robot colony. This architecture is basedon representation of the likes and dislikes of the robots. It is shown thatthe novel architecture is not only robust, but also provides the robots withintelligent adaptive behavior. This objective is achieved by utilization ofthree different techniques of neural networks, machine learning, and geneticalgorithms. Each of these methodologies are applied to the tropismarchitecture, resulting in improvements in the task performance of the robotteam, demonstrating the adaptability and robustness of the proposed controlsystem.
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  • 38
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 25 (1999), S. 43-59 
    ISSN: 1573-0409
    Schlagwort(e): PID control ; GAs ; neural networks ; multivariable systems
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract When genetic algorithms (GAs) are applied for PID parameter tuning, since the PID parameters are adjusted almost randomly, it is possible that the plant will be damaged due to abrupt changes in PID parameters. To solve this problem, a neural network will be used to model the plant and the genetic tuning procedure will be performed on the neural network instead of the plant. After determining the PID parameters in this off-line manner, these gains are then applied to the plant for on-line control. Moreover, considering that the neural network model may not be accurate enough, a method is also proposed for on-line fine-tuning of PID parameters. To show the validity of the proposed method, a seesaw system that has one input and two outputs will be used for experimental evaluation
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  • 39
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 20 (1997), S. 157-180 
    ISSN: 1573-0409
    Schlagwort(e): robot ; PID control ; neural networks ; learning ; generalization
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract In this article, an approach for improving the performance of industrialrobots using multilayer feedforward neural networks is presented. Thecontroller based on this approach consists of two main components: a PIDcontrol and a neural network. The function of the neural network is tocomplement the PID control for the specific purpose of improving theperformance of the system over time. Analytical and experimental resultsconcerning this synthesis of neural networks and PID control are presented.The analytical results assert that the performance of PID-controlledindustrial robots can be improved through proper utilization of the learningand generalization ability of neural networks. The experimental results,obtained through actual implementation using a commercial industrial robot,demonstrate the effectiveness of such control synthesis for practicalapplications. The results of this work suggest that neural networks could beadded to existing PID-controlled industrial robots for performanceimprovement.
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  • 40
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 18 (1997), S. 367-397 
    ISSN: 1573-0409
    Schlagwort(e): virtual reality ; human-machine system ; robotics ; neural networks ; collision avoidance ; trajectory planning ; conglomerate of spheres
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract This paper describes how virtual tools that represent real robot end-effectors are used in conjunction with a generalized conglomerate-of-spheres approach to collision avoidance in such a way that telerobotic trajectory planning can be accomplished using simple gesture phrases such as ‘put that there while avoiding that’. In this concept, an operator (or set of collaborators) need not train for cumbersome telemanipulation on several multiple-link robots, nor do robots need a priori knowledge of operator intent and exhaustive algorithms for evaluating every aspect of a detailed environment model. The human does what humans do best during task specification, while the robot does what machines do best during trajectory planning and execution. Four telerobotic stages were implemented to demonstrate this strategic supervision concept that will facilitate collaborative control between humans and machines. In the first stage, virtual reality tools are selected from a ‘toolbox’ by the operator(s) and then these virtual tools are computationally interwoven into the live video scene with depth correlation. Each virtual tool is a graphic representation of a robot end-effector (gripper, cutter, or other robot tool) that carries tool-use attributes on how to perform a task. An operator uses an instrumented glove to virtually retrieve the disembodied tool, in the shared scene, and place it near objects and obstacles while giving key-point gesture directives, such as ‘cut there while avoiding that’. Collaborators on a network may alter the plan by changing tools or tool positioning to achieve preferred results from their own perspectives. When parties agree, from wherever they reside geographically, the robot(s) create and execute appropriate trajectories suitable to their own particular links and joints. Stage two generates standard joint-interpolated trajectories, and later creates potential field trajectories if necessary. Stage three tests for collisions with obstacles identified by the operator and modeled as conglomerates of spheres. Stage four involves automatic grasping (or cutting etc.) once the robot camera acquires a close-up view of the object during approach. In this paper particular emphasis is placed on the conglomerate-of-spheres approach to collision detection as integrated with the virtual tools concept for a Puma 560 robot by the Virtual Tools and Robotics Group in the Computer Integrated Manufacturing Laboratory at The Pennsylvania State University (Penn State).
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  • 41
    Digitale Medien
    Digitale Medien
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    Journal of intelligent and robotic systems 21 (1998), S. 155-165 
    ISSN: 1573-0409
    Schlagwort(e): neural networks ; synchronization of signals ; time series
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract Before any multivariate analysis scheme (using typicaly simultaneous measurements) it is useful to readjust in time each measurement so that each one refer to the same element of the model. This problem arises frequently in the modelization of an industrial process. It is sometimes possible to propose a dynamic model after a microscopic study of displacements of matter. However such a study is very complex and cannot be conceived by a machine analysis of the data. In this article we present a practical self-adapting methodology which automates the synchronization of captors. This method use a recurrent neural network for the self auto-adaptivity of the synchronization. This network is model and tune automaticaly by an initialization process based on the local stationary phenomena appearing in measurements.
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  • 42
    Digitale Medien
    Digitale Medien
    Springer
    Journal of intelligent and robotic systems 22 (1998), S. 117-127 
    ISSN: 1573-0409
    Schlagwort(e): path planning for robots ; stochastic uncertainty ; real-time computation ; B-splines ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract The problem of adaptive trajectory planning for robots under stochastic uncertainty is considered, where new information about the robots and their environment is presented on-line. Solving the problem numerically by means of spline approximation and by applying the method of neural networks, the optimal control can be calculated in real-time. Some numerical results are presented.
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  • 43
    Digitale Medien
    Digitale Medien
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    Applied mathematics and mechanics 19 (1998), S. 457-462 
    ISSN: 1573-2754
    Schlagwort(e): neural networks ; equilibrium ; stability
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau , Mathematik , Physik
    Notizen: Abstract In this paper, some sufficient conditions are obtained for the global asymptotic stability of the equilibrium of neural networks with interneuronal transmission delays of the type $$x'_i (t) = - b_i x_i (t) + \sum\limits_{j = 1}^n {\omega _{\ddot y} f_j (x_j (t - \tau _j )) + p_i (t 〉 0;i = 1,2, \cdots ,n)} $$
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  • 44
    Digitale Medien
    Digitale Medien
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    Applied mathematics and mechanics 20 (1999), S. 721-728 
    ISSN: 1573-2754
    Schlagwort(e): Saint-Venant's principle ; variational principles ; neural networks ; computational stock market ; F224.9
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau , Mathematik , Physik
    Notizen: Abstract In this paper, three basic principles for computational stock market are proposed namely, “the Nearest-Time Principle” (NTP), “the Following Tendency Principle” (FTP), and “the Variational Principle on Difference of Supply and Demand” (VPDSD). The issue, expression, mathematical description and applications of these principles are stated. These applications involve the use in neural networks, basic equations of computational stock market, and the prediction of equilibrium price of stocks etc.
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  • 45
    Digitale Medien
    Digitale Medien
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    Autonomous robots 5 (1998), S. 215-231 
    ISSN: 1573-7527
    Schlagwort(e): mobile robot ; road following ; multi-sensor integration ; visual feedback ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract Mobile robots capable of moving autonomously in more or less structured environments are being increasingly employed in the automation of certain industrial processes. Along these lines, the authors constructed a platform, on the base of a commercial industrial truck, provided with sufficient autonomy to carry out tasks within an industrial environment (VIA: Autonomous Industrial Vehicle). One of the sensor systems used in the truck is a system of artificial vision which enables it to move on asphalted surfaces both in open environments (roads) and closed ones, seeking the markings which most easily allow it to determine the path marked in the images. The system for following roads is capable of following painted lines, determining the sides of the road by texture analysis or determining the minimum width of the road for the robot to pass, according to the circumstances. A model of the road predicts its situation and enables a decision to be made on whether the information provided by the algorithm is reliable or not. At the same time, a neural network is trained with the results obtained by any of the previous algorithms, in such a way that when the training process converges the network takes over the steering of the truck. The vision system, composed of a CCD colour camera and a “frame grabber” installed in a PCI slot of a Pentium 120 PC, provides a path every 100 ms, which allows the industrial truck to be steered at its maximum speed of 10 m/s.
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  • 46
    Digitale Medien
    Digitale Medien
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    Autonomous robots 5 (1998), S. 239-251 
    ISSN: 1573-7527
    Schlagwort(e): robot learning ; concept learning ; neural networks
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract Concept learning in robotics is an extremely challenging problem: sensory data is often high dimensional, and noisy due to specularities and other irregularities. In this paper, we investigate two general strategies to speed up learning, based on spatial decomposition of the sensory representation, and simultaneous learning of multiple classes using a shared structure. We study two concept learning scenarios: a hallway navigation problem, where the robot has to induce features such as “opening” or “wall”. The second task is recycling, where the robot has to learn to recognize objects, such as a “trash can”. We use a common underlying function approximator in both studies in the form of a feedforward neural network, with several hundred input units and multiple output units. Despite the high degree of freedom afforded by such an approximator, we show the two strategies provide sufficient bias to achieve rapid learning. We provide detailed experimental studies on an actual mobile robot called PAVLOV to illustrate the effectiveness of this approach.
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  • 47
    Digitale Medien
    Digitale Medien
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    Autonomous robots 5 (1998), S. 381-394 
    ISSN: 1573-7527
    Schlagwort(e): mobile robots ; neural networks ; machine vision ; robot learning
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract This paper presents the design, implementation and evaluation of a trainable vision guided mobile robot. The robot, CORGI, has a CCD camera as its only sensor which it is trained to use for a variety of tasks. The techniques used for training and the choice of natural light vision as the primary sensor makes the methodology immediately applicable to tasks such as trash collection or fruit picking. For example, the robot is readily trained to perform a ball finding task which involves avoiding obstacles and aligning with tennis balls. The robot is able to move at speeds up to 0.8 ms-1 while performing this task, and has never had a collision in the trained environment. It can process video and update the actuators at 11 Hz using a single $20 microprocessor to perform all computation. Further results are shown to evaluate the system for generalization across unseen domains, fault tolerance and dynamic environments.
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  • 48
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    The international journal of advanced manufacturing technology 13 (1997), S. 587-599 
    ISSN: 1433-3015
    Schlagwort(e): Fuzzy logic ; neural networks ; Signal-to-noise ratio ; Taguchi parameter design
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Maschinenbau
    Notizen: Abstract Fuzzy nets have been proposed to combine the learning ability of neural networks and the reasoning ability of fuzzy logic to deal with complex control systems. This paper presents a systematic way of identifying the significant factors and optimising the performance of a fuzzy-nets application. To present the methodology, a model of a truck backing up has been evaluated. Four factors were considered: 1. The number of training sets. 2. The number of fuzzy regions. 3. The membership functions. 4. The fuzzy reasoning methods which would affect the performance of the fuzzy-nets training scheme in nonlinear applications. The Taguchi parameter design was implemented with anL 9 (34) orthogonal array to identify the optimal combination for training consideration. Both raw and signal-to-noise (S/N) ratios were evaluated to identify the optimal combination for the performance of fuzzy-nets training with very limited variation. The performance of the proposed fuzzy-nets scheme for the model of the truck backing up was represented by the average errors between the truck and loading dock: 0.178 units and 0.204 degrees. The results demonstrate that the Taguchi parameter design is a robust approach for optimising the performance of the fuzzy-nets training scheme.
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  • 49
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    Autonomous robots 7 (1999), S. 57-75 
    ISSN: 1573-7527
    Schlagwort(e): sensor-based manipulators ; multi-goal reaching tasks ; reinforcement learning ; neural networks ; differential inverse kinematics
    Quelle: Springer Online Journal Archives 1860-2000
    Thema: Informatik , Maschinenbau
    Notizen: Abstract Our work focuses on making an autonomous robot manipulator learn suitable collision-free motions from local sensory data while executing high-level descriptions of tasks. The robot arm must reach a sequence of targets where it undertakes some manipulation. The robot manipulator has a sonar sensing skin covering its links to perceive the obstacles in its surroundings. We use reinforcement learning for that purpose, and the neural controller acquires appropriate reaction strategies in acceptable time provided it has some a priori knowledge. This knowledge is specified in two main ways: an appropriate codification of the signals of the neural controller—inputs, outputs and reinforcement—and decomposition of the learning task. The codification facilitates the generalization capabilities of the network as it takes advantage of inherent symmetries and is quite goal-independent. On the other hand, the task of reaching a certain goal position is decomposed into two sequential subtasks: negotiate obstacles and move to goal. Experimental results show that the controller achieves a good performance incrementally in a reasonable time and exhibits high tolerance to failing sensors.
    Materialart: Digitale Medien
    Standort Signatur Erwartet Verfügbarkeit
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