ISSN:
1573-0409
Keywords:
modular neural networks
;
classification
;
cooperative decision making
;
performance comparison
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
,
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
Abstract There is a wide variety of Modular Neural Network (MNN) classifiers in the literature. They differ according to the design of their architecture, task-decomposition scheme, learning procedure, and multi-module decision-making strategy. Meanwhile, there is a lack of comparative studies in the MNN literature. This paper compares ten MNN classifiers which give a good representation of design varieties, viz., Decoupled; Other-output; ART-BP; Hierarchical; Multiple-experts; Ensemble (majority vote); Ensemble (average vote); Merge-glue; Hierarchical Competitive Neural Net; and Cooperative Modular Neural Net. Two benchmark applications of different degree and nature of complexity are used for performance comparison, and the strength-points and drawbacks of the different networks are outlined. The aim is to help a potential user to choose an appropriate model according to the application in hand and the available computational resources.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1023/A:1007925203918
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