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Proceedings Paper

Training radial basis function classifiers with Gaussian kernel clustering and fuzzy decision technique
Author(s): Yuntao Qian; Weixing Xie
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Paper Abstract

Radial basis function (RBF) neural networks have been used extensively in many applications for their simple architecture and fast learning. This paper principally discusses the training problem of RBF classifiers which can be used for classification. For RBF classifiers, how to correctly initialize the number of network hidden nodes and their parameters is very important. Genetic-based Gaussian kernel clustering method and fuzzy decision technique are explored to complete this work. Then the network is trained further with back propagation learning algorithm in order to attain optimal performance. Results from the typical experiments are used to illustrate the power and efficiency of the method.

Paper Details

Date Published: 28 August 1995
PDF: 6 pages
Proc. SPIE 2620, International Conference on Intelligent Manufacturing, (28 August 1995); doi: 10.1117/12.217541
Show Author Affiliations
Yuntao Qian, Xidian Univ. (China)
Weixing Xie, Xidian Univ. (China)

Published in SPIE Proceedings Vol. 2620:
International Conference on Intelligent Manufacturing
Shuzi Yang; Ji Zhou; Cheng-Gang Li, Editor(s)

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