Searching for a solution to the automatic RBF network design problem

While amazing applications have been demonstrated in di+erent science and engineering ,elds using neural networks and evolutionary approaches, one of the key elements of their further acceptance and proliferation is the study and provision of procedures for the automatic design of neural architectures and associated learning methods, i.e., in general, the study of the systematic and automatic design of arti,cial brains. In this contribution, connections between conventional techniques of pattern recognition, evolutionary approaches, and newer results from computational and statistical learning theory are brought together in the context of the automatic design of RBF regression networks. c 2002 Elsevier Science B.V. All rights reserved.

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