Construction of fuzzy inference rules by NDF and NDFL

AbstractWhereas conventional fuzzy reasoning lacks determining membership functions, a neural network driven fuzzy reasoning (NDF) capable of determining membership functions uniquely by an artificial neural network is formulated. In an NDF algorithm the optimum membership function in the antecedent part of fuzzy inference rules is determined by a neural network, while in the consequent parts an amount of reasoning for each rule is determined by other plural neural networks. On the other hand, we propose a new algorithm that can adjust inference rules to compensate for a change of inference environment. We call this algorithm a neural network driven fuzzy reasoning with learning function (NDFL). NDFL can determine the optimal membership function and obtain the coefficients of linear equations in the consequent parts by the searching function of the pattern search method. In this paper, inference rules for making a pendulum stand up from its lowest suspended point ar3 determined by the NDF algorithm for verifying its effectiveness. The NDFL algorithm is formulated and applied to a simple numerical example to demonstrate its effectiveness