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Proceedings Paper

POP-Yager: a novel self-organizing fuzzy neural network based on the Yager inference
Author(s): Chai Quek; Abdul Wahab; Singh Aarit
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Paper Abstract

A Pseudo-Outer Product based Fuzzy Neural Network using the Yager Rule of Inference called the POP-Yager FNN is proposed in this paper. The proposed POP-Yager FNN training consists of two phases: the fuzzy membership derivation phase using the Modified Learning Vector Quantization (MLVQ) method; and the rule identification phase using the novel one-pass LazyPOP learning algorithm. The proposed two-phase learning process effectively constructs the membership functions and identifies the fuzzy rules. Extensive experimental results based on the classification performance of the POP-Yager FNN using the Anderson's Iris data are presented for discussion. Results show that the POP-Yager FNN possesses excellent recall and generalization abilities.

Paper Details

Date Published: 13 October 2000
PDF: 12 pages
Proc. SPIE 4120, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III, (13 October 2000); doi: 10.1117/12.403624
Show Author Affiliations
Chai Quek, Nanyang Technological Univ. (Singapore)
Abdul Wahab, Nanyang Technological Univ. (Singapore)
Singh Aarit, Nanyang Technological Univ. (Singapore)


Published in SPIE Proceedings Vol. 4120:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation III
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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