Fuzzy Logic Inference Neural Networks

James M. Keller; Ronald R. Yager

doi:10.1117/12.969771

1 March 1990 Fuzzy Logic Inference Neural Networks

James M. Keller, Ronald R. Yager

Proceedings Volume 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques; (1990) https://doi.org/10.1117/12.969771
Event: 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, 1989, Philadelphia, PA, United States

Abstract

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.

Citation Download Citation

James M. Keller and Ronald R. Yager "Fuzzy Logic Inference Neural Networks", Proc. SPIE 1192, Intelligent Robots and Computer Vision VIII: Algorithms and Techniques, (1 March 1990); https://doi.org/10.1117/12.969771

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