POP-Yager: a novel self-organizing fuzzy neural network based on the Yager inference
Chai Hiok Quek, Abdul Wahab, Singh Aarit · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
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.