ADD-MULT FUZZY NEURAL NETWORK AND COMPARISON WITH SIX REPRESENTATIVE INFERENCE METHODS

This paper presents a model of Add-Mult Fuzzy Neural Network (AMFNN) and the model’s architecture as well. Here, Error Back Propagation algorithm for AMFNN is presented according to the Gradient Descent Method. The result Compared with six representative fuzzy inference methods shows that AMFNN has high reasoning precision, wide application scope, strong generalization capability and easy implementation characteristics. Consequently, AMFNN has vast application prospect.