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  • Articles  (18)
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  • Articles  (18)
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  • Springer  (18)
  • American Geophysical Union
  • American Institute of Physics (AIP)
  • American Physical Society
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  • 2000-2004
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  • 1
    Electronic Resource
    Electronic Resource
    Committees, collectives and individuals: Expert visual classification by neural network (1997)
    Springer
    Neural computing & applications 5 (1997), S. 99-105 
    Details
    ISSN: 1433-3058
    Keywords: Neural networks ; Taxonomic expertise ; Committee classification
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract It has been established that committee classifiers, in which the outputs of different, individual network classifiers are combined in various ways, can produce better accuracy than the best individual in the committee. We describe results showing that these advantages are obtained when neural networks are applied to a taxonomic problem in marine science: the classification of images of marine phytoplankton. Significant benefits were found when individual networks, trained on different classes of input, having comparable individual performances, were combined. Combining networks of very different accuracy did not improve performance when measured against the best single network, but nor was it reduced. An alternative architecture, which we term a collective machine, in which the different data types are combined in a single network, was found to have significantly better accuracy than the committee machine architectures. The performance gains and resilience to non-discriminatory types of data suggest the techniques have great utility in the development of general purpose, network classifiers.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01501174
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  • 2
    Electronic Resource
    Electronic Resource
    Neuro-Resistive Grid approach to trainable controllers: A pole balancing example (1997)
    Springer
    Neural computing & applications 5 (1997), S. 33-44 
    Details
    ISSN: 1433-3058
    Keywords: Inverted pendulum problem ; Reinforcement learning ; Learning control ; Nonlinear control ; Neural networks ; Neuro-resistive grid method ; Value map
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A new neural network approach is described for the task of pole-balancing, considered a benchmark learning control problem. This approach combines Barto, Sutton and Anderson's [1] Associative Search Element (ASE) with a Neuro-Resistive Grid (NRG) [2] acting as Adaptive Critic Element (ACE). The novel feature in NRG is that it provides evaluation of a state based on propagation of the failure information to the neighbours in the grid. NRG is updated only on a failure, and provides ASE with a continuous internal reinforcement signal by comparing the value of the present state to the previous state. The resulting system learns more rapidly and with fewer computations than that of Barto et al.[1]. To establish a uniform basis of comparison of algorithms for pole balancing, both the systems are simulated using benchmark parameters and tests specified in Geva and Sitte [3].
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01414101
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  • 3
    Electronic Resource
    Electronic Resource
    Neural networks applied to ultrasonic tomographic image reconstruction (1997)
    Springer
    Neural computing & applications 5 (1997), S. 106-123 
    Details
    ISSN: 1433-3058
    Keywords: Image reconstruction ; Neural networks ; Ultrasonic tomography ; Polymer composites
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A neural network system has been developed which can reconstruct images of defects within fibre reinforced polymer composite samples. This paper discusses the problems associated with the image reconstruction of the ultrasonic data using neural networks, together with various methods adopted to improve the performance of the neural network system, including a modification to the error backpropagation algorithm.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01501175
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  • 4
    Electronic Resource
    Electronic Resource
    Classification of faults in gearboxes — pre-processing algorithms and neural networks (1997)
    Springer
    Neural computing & applications 5 (1997), S. 160-183 
    Details
    ISSN: 1433-3058
    Keywords: Fault detection ; Gearbox vibration ; Machinery diagnostics ; Neural networks ; Pattern recognition ; Pre-processing algorithms
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Classical signal processing techniques when combined with pattern classification analysis can provide an automated fault detection procedure for machinery diagnostics. Artificial neural networks have recently been established as a powerful method of pattern recognition. The neural networkbased fault detection approach usually requires preprocessing algorithms which enhance the fault features, reducing their number at the same time. Various timeinvariant and timevariant signal preprocessing algorithms are studied here. These include spectral analysis, time domain averaging, envelope detection, Wigner-Ville distributions and wavelet transforms. A neural network pattern classifier with preprocessing algorithms is applied to experimental data in the form of vibration records taken from a controlled tooth fault in a pair of meshing spur gears. The results show that faults can be detected and classified without errors.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01413861
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  • 5
    Electronic Resource
    Electronic Resource
    Deformation invariant visual object recognition: Experiments with a self-organising neural architecture (1997)
    Springer
    Neural computing & applications 6 (1997), S. 79-90 
    Details
    ISSN: 1433-3058
    Keywords: Computer vision ; Neural networks ; Object classification ; On-line training ; Pattern recognition ; Self-organisation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A self-organising neural network architecture for grey-scale visual object rcognition is presented. The network is composed of three processing layers with an architecture designed to give deformation tolerance. The processing layers involve feature extraction, sub-pattern detection and classification. Training is generally performed on-line in an unsupervised manner, classes being created when objects are presented that cannot be classified. The results given show the effect of the two discrimination parameters when the network is applied to two very different sets of images, namely hand written numerals and hand gestures images. The sensitivity of the network to the parameters that govern the size of detectable patterns and the areas over which they are detected is also tested. The robustness of the network to the order of image presentation is also demonstrated. The results show that parameter choice is not critical and heuristically chosen parameters provide near optimum performance.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01414005
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  • 6
    Electronic Resource
    Electronic Resource
    Application of neural network computing to thermal non-destructive evaluation (1997)
    Springer
    Neural computing & applications 6 (1997), S. 148-157 
    Details
    ISSN: 1433-3058
    Keywords: Bayesan interpretation ; Classification ; Neural networks ; Thermal Non-destructive evaluation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A methodological study on the use of neural networks for defect characterisation by means of a thermal method is presented. Neural networks are used here as defect classifiers, based on the infrared emission of the target object after heating. In this kind of application, there is a high degree of uncertainty in defect class boundaries due to several factors, such as the noise in the measurement, the uneven heating of the target object and the anisotropies in its thermal conductivity. For this reason, the classical ‘1 of N’ coding scheme during training did not provide satisfactory results. Much better results have instead been obtained using a smoother activation function for the output units during training. The non-destructive evaluation of material using neural networks proved extremely satisfactory, especially when compared to the classical procedures of thermographic analysis.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01413826
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  • 7
    Electronic Resource
    Electronic Resource
    Comparison of neural networks and statistical models to predict gestational age at birth (1997)
    Springer
    Neural computing & applications 6 (1997), S. 158-164 
    Details
    ISSN: 1433-3058
    Keywords: High risk pregnancy ; Logistic regression ; Neural networks ; Pre-term delivery ; Receiver Operating Characteristic curve
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract The aim of this study was to produce models for the prediction of high risk pregnancies, with particular emphasis on pre-term delivery. Neural network and logistic regression models have been developed utilising pregnancy and delivery data spanning a period of seven years. Five input factors were used as explanatory variables: age, number of previous still births, gestational age at first clinical assessment, diabetes and a measure of socio-economic status. There was little difference between average model performance for the two techniques: optimal neural network performance was achieved with a fully connected feed forward network comprising a single hidden layer of three nodes and single output node. This produced a Receiver Operating Characteristic (ROC) curve area of 0.700. The ROC area for logistic regression models was 0.695. The performance of these models reflected weak associations within the data. However, performance is encouraging given the relatively limited number of predictive inputs.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01413827
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  • 8
    Electronic Resource
    Electronic Resource
    Neural clustering networks based on global optimisation of prototypes in metric spaces (1997)
    Springer
    Neural computing & applications 5 (1997), S. 2-13 
    Details
    ISSN: 1433-3058
    Keywords: Global optimisation ; Clustering ; Unsupervised learning ; Neural networks ; Random optimisation ; EEG processing
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract The utilisation of clustering algorithms based on the optimisation of prototypes in neural networks is demonstrated for unsupervised learning. Stimulated by common clustering methods of this type (learning vector quantisation [LVQ, GLVQ] and K-means) a globally operating algorithm was developed to cope with known shortcomings of existing tools. This algorithm and K-means (for the common methods) were applied to the problem of clustering EEG patterns being pre-processed. It can be shown that the algorithm based on global random optimisation may find an optimal solution repeatedly, whereas K-means provides different sub-optimal solutions with respect to the quality measure defined as objective function. The results are presented. The performance of the algorithms is discussed.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01414098
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  • 9
    Electronic Resource
    Electronic Resource
    Neural simulation of acquired and progressive language disorders (1997)
    Springer
    Neural computing & applications 5 (1997), S. 215-223 
    Details
    ISSN: 1433-3058
    Keywords: Neural networks ; Cognitive modelling ; Language disorders ; Multi-network architectures ; Simulation of change ; Self-organisation ; LISA ; Complex systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper describes a novel approach to the simulation of language disorders, based upon the notion of a multi-network architecture —a set of autonomous neural networks which have been linked in some manner to perform a complex function that cannot readily be performed by any one network alone. The merits of this approach have been assessed by mapping a neuropsychological model of single-word language processing onto a multi-network architecture. Language disorders may be simulated by damaging, or ‘lesioning’, one or more component networks. Our attempts to simulate two specific language disorders, semantic dementia and deep dysphasia, are described. The relative success of our simulation work is encouraging, and leads us to conclude that a multi-network approach to the simulation of cognitive function and dysfunction offers a valid alternative to the ‘traditional’ single-network based perspective.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01424226
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  • 10
    Electronic Resource
    Electronic Resource
    Blind source separation with convolutive noise cancellation (1997)
    Springer
    Neural computing & applications 6 (1997), S. 127-141 
    Details
    ISSN: 1433-3058
    Keywords: Adaptive noise cancellation ; Blind separation ; FIR filters ; ICA ; Learning algorithms ; Neural networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract On-line adaptive learning algorithms for cancellation of additive, convolutive noise from linear mixtures of sources with a simultaneous blind source separation are developed. Associated neural network architectures are proposed. A simple convolutive noise model is assumed, i.e. the unknown additive noise in each channel is a (FIR) filtering version of environmental noise, where some convolutive reference noise is measurable. Two approaches are considered: in the first, the noise is cancelled from the linear mixture of source signals as pre-processing, after that the source signals are separated; in the second, both source separation and additive noise cancellation are performed simultaneously. Both steps consist of adaptive learning processes. By computer simulation experiments, it was found that the first approach is applicable for a large amount of noise, whereas in the second approach, a considerable increase of the convergence speed of the separation process can be achieved. Performance and validity of the proposed approaches are demonstrated by extensive computer simulations.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01413824
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  • 11
    Electronic Resource
    Electronic Resource
    The impact of neural networks in finance (1997)
    Springer
    Neural computing & applications 6 (1997), S. 193-200 
    Details
    ISSN: 1433-3058
    Keywords: Economic modelling ; Finance ; Neural networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract The financial industry is becoming more and more dependent on advanced computer technologies in order to maintain competitiveness in a global economy. Neural networks represent an exciting technology with a wide scope for potential applications, ranging from routine credit assessment operations to driving of large scale portfolio management strategies. Some of these applications have already resulted in dramatic increases in productivity. This paper brings together, from diverse sources, a collection of current research issues on neural networks in the financial domain. It examines a range of neural network systems related to financial applications from different levels of maturity to fielded products. It discusses the success rate of the neural network systems, and their performance in resolving particular financial problems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01501506
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  • 12
    Electronic Resource
    Electronic Resource
    Fuzzy assisted learning in backpropagation neural networks (1997)
    Springer
    Neural computing & applications 6 (1997), S. 238-244 
    Details
    ISSN: 1433-3058
    Keywords: Adaptive algorithms ; Back propagation ; Fuzzy control ; Improved learning ; Neural networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract This paper reports on studies to overcome difficulties associated with setting the learning rates of backpropagation neural networks by using fuzzy logic. Building on previous research, a fuzzy control system is designed which is capable of dynamically adjusting the individual learning rates of both hidden and output neurons, and the momentum term within a back-propagation network. Results show that the fuzzy controller not only eliminates the effort of configuring a global learning rate, but also increases the rate of convergence in comparison with a conventional backpropagation network. Comparative studies are presented for a number of different network configurations. The paper also presents a brief overview of fuzzy logic and backpropagation learning, highlighting how the two paradigms can enhance each other.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01501510
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  • 13
    Electronic Resource
    Electronic Resource
    Engine Control using Neural Networks: A New Method in Engine Management Systems (1997)
    Springer
    Meccanica 32 (1997), S. 423-430 
    Details
    ISSN: 1572-9648
    Keywords: Neural networks ; Combustion control ; Spark ignition ; Internal combustion engines ; Automotive applications
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics , Physics
    Notes: Abstract To be able to meet the demands of low emissions and fuel consumption ofmodern combustion engines, new ways have to be found to control thecombustion. We use new sensors to measure the pressure in the combustionchamber and analyze this signal with a neural network in order to receiveseveral form factors which can be used to control the ignition timing. Theneural network is trained off line with measured data and used on line toderive the form factors. The proposed algorithm can be computed in real timeon conventional digital signal processors and adapted to new engines withvery little effort.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1004203832719
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  • 14
    Electronic Resource
    Electronic Resource
    A field trial using Artificial Neural Networks to predict psychiatric in-patient Length-of-stay (1997)
    Springer
    Neural computing & applications 5 (1997), S. 184-193 
    Details
    ISSN: 1433-3058
    Keywords: Neural networks ; Length-of-stay ; Psychiatry ; Resource utilization ; Back propagation ; Field study
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract Demands for health care reform will increase service utilization, much of which will fall on a system of expanded primary care providers, many of whom will not be specialists in psychiatry. These providers will need tools to augment their decision-making process. In this paper, we explore the use of Artificial Neural Networks (ANNs) in three different field sites to predict inpatient psychiatric Length-Of-Stay (LOS). This study describes the development and implementation of a runtime system in three different psychiatric facilities. Data was collected at these respective sites using the runtime system, and then this data was used to retrain the networks to determine if site-specific data would improve accuracy of prediction of LOS. The results indicate that ANNs trained with state hospital data could accurately predict LOS in two different community hospital psychiatric units. When the respective ANNs were retrained with approximately 10% new data from these specific hospitals, rates of improvement ranged from 3% to 15%. Our findings demonstrate that an ANN can adapt to different treatment settings and, when retrained, significantly improve prediction of LOS. Prediction rates by the ANN after retraining are comparable to results of a clinical team.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01413862
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  • 15
    Electronic Resource
    Electronic Resource
    Temperature analysis of Coherent Anti-Stokes Raman spectra using a neural network approach (1997)
    Springer
    Neural computing & applications 5 (1997), S. 248-257 
    Details
    ISSN: 1433-3058
    Keywords: Neural networks ; CARS ; Spectroscopy ; Combustors
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A neural network trained with clustered data has been applied to the extraction of temperature from vibrational Coherent Anti-Stokes Raman (CARS) spectra of nitrogen. CARS is a non-intrusive thermometry technique applied in practical combustors in industry. The advantages of clustering of training data over training with unprocessed calculated spectra is described. The method is applied to CARS data from an isothermal furnace and a liquid kerosene fuelled aeroengine combustor sector rig. Resulting temperatures have been compared with values extracted from the data using conventional least squares fitting and, where possible, mean temperatures measured by pyrometer and blackbody cavity probe. The main advantage of the neural network method is speed, with the potential for online temperature extraction at the spectral acquisition rate of 10 Hz using standard PC hardware.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01424230
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  • 16
    Electronic Resource
    Electronic Resource
    Genetic design of real-time neural network controllers (1997)
    Springer
    Neural computing & applications 6 (1997), S. 12-18 
    Details
    ISSN: 1433-3058
    Keywords: Flow systems ; Genetic algorithms ; Neural networks ; Real-time control ; Reinforcement learning
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract The use of genetic algorithms to design neural networks for real-time control of flows in sewerage networks is discussed. In many control applications, standard supervised learning techniques (such as back-propagation) cannot be used through lack of training data. Reinforcement learning techniques, such as genetic algorithms, are a computationally-expensive but viable alternative if a simulator is available for the system in question. The paper briefly describes why genetic algorithms and neural networks were selected, then reports the results of a feasibility study. This demonstrates that the approach does indeed have merits. The implications of high computational cost are discussed, in terms of scaling up to significantly complex problems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01670149
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  • 17
    Electronic Resource
    Electronic Resource
    The prediction of protein secondary structure with a Cascade Correlation Learning Architecture of neural networks (1997)
    Springer
    Neural computing & applications 6 (1997), S. 57-62 
    Details
    ISSN: 1433-3058
    Keywords: Cascade correlation learning algorithm ; Neural networks ; Pattern recognition ; Predictive methods ; Protein secondary structure prediction
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science , Mathematics
    Notes: Abstract A Cascade Correlation Learning Architecture (CCLA) of neural networks is tested on the task of predicting the secondary structure of proteins. The results are compared with those obtained with Neural Networks (NN) trained with the back-propagation algorithm (BPNN) and generated with genetic algorithms. CCLA proceeds towards the global minimum of the error function more efficiently than BPNN. However, only a slight improvement in the average efficiency value is noticeable (61.82% as compared with 61.61% obtained with BPNN). The values of the three correlation coefficients for the discriminated secondary structures are also rather similar (Ct8,C α ,C β and Ccoil are 0.36, 0.29 and 0.36 with CCLA, and 0.36, 0.31 and 0.35 with BPNN). This indicates that the efficiency of the prediction does not depend upon the training algorithm, and confirms our previous observation that when single sequences are used as input code to the network system, different NN architectures can perform similarly.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01670152
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  • 18
    Electronic Resource
    Electronic Resource
    Analog VLSI Stochastic Perturbative Learning Architectures (1997)
    Springer
    Analog integrated circuits and signal processing 13 (1997), S. 195-209 
    Details
    ISSN: 1573-1979
    Keywords: Neural networks ; neuromorphic engineering ; reinforcement learning ; stochastic approximation
    Source: Springer Online Journal Archives 1860-2000
    Topics: Electrical Engineering, Measurement and Control Technology
    Notes: Abstract We present analog VLSI neuromorphic architectures fora general class of learning tasks, which include supervised learning,reinforcement learning, and temporal difference learning. Thepresented architectures are parallel, cellular, sparse in globalinterconnects, distributed in representation, and robust to noiseand mismatches in the implementation. They use a parallel stochasticperturbation technique to estimate the effect of weight changeson network outputs, rather than calculating derivatives basedon a model of the network. This “model-free” technique avoidserrors due to mismatches in the physical implementation of thenetwork, and more generally allows to train networks of whichthe exact characteristics and structure are not known. With additionalmechanisms of reinforcement learning, networks of fairly generalstructure are trained effectively from an arbitrarily suppliedreward signal. No prior assumptions are required on the structureof the network nor on the specifics of the desired network response.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008252532342
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