Title: Simplification of fuzzy-neural systems using similarity analysis
Authors: Chao, CT
Chen, YJ
Teng, CC
交大名義發表
電控工程研究所
National Chiao Tung University
Institute of Electrical and Control Engineering
Issue Date: 1-Apr-1996
Abstract: This paper presents a fuzzy neural network system (FNNS) for implementing fuzzy inference systems. In the FNNS, a fuzzy similarity measure for fuzzy rules is proposed to eliminate redundant fuzzy logical rules, so that the number of rules in the resulting fuzzy inference system will be reduced. Moreover, a fuzzy similarity measure for fuzzy sets that indicates the degree to which two fuzzy sets are equal is applied to combine similar input linguistic term nodes. Thus we obtain a method for reducing the complexity of a fuzzy neural network. We also design a new and efficient on-line initialization method for choosing the initial parameters of the FNNS. a computer simulation is presented to illustrate the performance and applicability of the proposed FNNS. The results indicates that the FNNS still has desirable performance under fewer fuzzy logical rules and adjustable parameters.
URI: http://dx.doi.org/10.1109/3477.485887
http://hdl.handle.net/11536/1378
ISSN: 1083-4419
DOI: 10.1109/3477.485887
Journal: IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
Volume: 26
Issue: 2
Begin Page: 344
End Page: 354
Appears in Collections:Articles


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  1. A1996UD02400015.pdf