Fuzzy Logic Inference Neural Networks
James M. Keller, Ronald R. Yager · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1990
Fuzzy Logic has gained increased attention as a methodology for managing uncertainty in a rule-based structure. In a fuzzy logic inference system, more rules can fire at any given time than in a crisp expert system and since the propositions are modelled as possibility distributions, there is a considerable computation load on the inference engine. In this paper, two neural network structures are proposed as a means of performing fuzzy logic inference. In the first structure, the knowledge of the rule (i.e., the antecedent and consequent clauses) are explicitly encoded in the weights of the net, whereas the second network in trained by example. Both theoretical properties and simulation results of these structures are included.