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

Content Provider	IEEE Xplore Digital Library
Author	Tsang, E.C.C. Yeung, D.S.
Copyright Year	1997
Description	Author affiliation: Dept. of Comput., Hong Kong Polytech., Hung Hom, Hong Kong (Tsang, E.C.C.)
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.
Starting Page	409
Ending Page	418
File Size	812800
Page Count	10
File Format	PDF
ISBN	0780337557
DOI	10.1109/KES.1997.619417
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1997-05-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Knowledge representation Production Fuzzy sets Knowledge acquisition Neural networks Fuzzy logic Hybrid intelligent systems Fuzzy control Knowledge engineering Computer networks
Content Type	Text
Resource Type	Article

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