A fuzzy neural network model for revising imperfect fuzzy rules

Abstract

In this paper, a fuzzy rule-based neural network model, named KBFNN, is proposed. The initial structure of KBFNN is constructed by existent partial fuzzy rules. These partial domain theories may be incorrect or incomplete. The domain theories are represented by fuzzy rules and are revised by neural network training. To construct KBFNN by fuzzy rules, two kinds of fuzzy neurons are proposed. They are S-neurons and G-neurons. The S-neurons perform similarity measure to compute the firing degrees of fuzzy rules. The G-neurons carry out the defuzzification of inference results. The KBFNN is capable of fuzzy inference. For processing fuzzy number efficiently, the LR-type fuzzy numbers are used. In the rule revision phase, a gradient descent revision algorithm is applied. An Inverted Pendulum System and a Knowledge-Based Evaluator are used to illustrate the workings of the proposed model. The experimental results are very encouraging.