NDLI: An enhanced clustering method for multiple shape basis function networks

Content Provider	IEEE Xplore Digital Library
Author	Jayasuriya, A. Halgamuge, S.K.
Copyright Year	1998
Description	Author affiliation: Inst. of Telecommun. Res., South Australia Univ., The Levels, SA, Australia (Jayasuriya, A.)
Abstract	By mapping a classifier type fuzzy system into a RBF neural network, tuning of the fuzzy system can be achieved. Using self evolving type RBF networks fuzzy classifiers including the rule base and membership functions can be created. Therefore, it is essential to achieve efficient classification rate in such neural networks. But it is also important to keep the number of automatically added neurons in the hidden layer to a minimum, since those neurons represent the fuzzy rules. This paper introduces a new algorithm of clustering for automatic creation of multiple shape basis function networks. They can be considered as a generalised form of RBF neural nets with base clusters of different shapes and sizes. The benchmark results show significant improvements and the correct balance between good classification results and the size of the created rule base.
Starting Page	7
Ending Page	11
File Size	517272
Page Count	5
File Format	PDF
ISBN	0780348591
ISSN	10987576
DOI	10.1109/IJCNN.1998.682227
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-05-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Clustering methods Fuzzy systems Neural networks Neurons Fuzzy neural networks Shape measurement Radial basis function networks Clustering algorithms Manufacturing Buildings
Content Type	Text
Resource Type	Article

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