POPFNN-AAR(S): a pseudo outer-product based fuzzy neural network

Authors:
C. Quek;R. W. Zhou
Affiliations:
Intelligent Syst. Lab., Nanyang Technol. Inst.;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
1999

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Abstract

A novel fuzzy neural network, the pseudo outer-product-based fuzzy neural network using the singleton fuzzifier together with the approximate analogical reasoning schema, is proposed in this paper. The network is referred to as the singleton fuzzifier POPFNN-AARS, the singleton fuzzifier POPFNN-AARS employs the approximate analogical reasoning schema (AARS) instead of the commonly used truth value restriction (TVR) method. This makes the structure and learning algorithms of the singleton fuzzifier POPFNN-AARS simple and conceptually clearer than those of the POPFNN-TVR model. Different similarity measures (SM) and modification functions (FM) for AARS are investigated. The structures and learning algorithms of the proposed singleton fuzzifer POPFNN-AARS are presented. Several sets of real-life data are used to test the performance of the singleton fuzzifier POPFNN-AARS and their experimental results are presented for detailed discussion