Acquiring and tuning knowledge representation parameters of fuzzy production rules using fuzzy expert networks

Abstract

Fuzzy production rules (FPRs) have been used and proved to be a very useful knowledge representation method to capture and represent fuzzy, uncertain, incomplete and vague domain expert knowledge. The knowledge representation capability of these FPRs could be enhanced if parameters like local weights, certainty factors or threshold values are incorporated. These parameters, together with the membership values of fuzzy sets, are, however, difficult to acquire or extract from domain experts during the knowledge acquisition phases and to fine-tune during the system upgrade and maintenance phase. In this paper, the fuzzy expert networks (FENs) proposed by the authors in the World Congress on Neural Networks, pp. 500-3 (1996) are extended so that they can acquire and fine-tune more knowledge representation parameters (KRPs). Local weight is added to the KRPs and incorporated into the antecedent part of a conjunctive FPR. The knowledge acquisition and refinement problems of these parameters and the membership values of fuzzy sets can be solved by using FENs which not only have the reasoning mechanism of a fuzzy expert system (FES) but also the learning capability of a neural network. An experiment is presented to illustrate the workability of our proposed method.