MODELING FUZZY PRODUCTION RULES WITH FUZZY EXPERT NETWORKS

The strength of a fuzzy expert system comes from its ability to handle imprecise, uncertain and vague information used by human experts whil e the power of neural networks lies in their learning, generalization and fault tolerance capabilities. There have already been many attempt s to model and formulate fuzzy production rules (FPRs) by using neural networks so that a new system, called a hybrid system, can be develop ed and will have the advantages of both. The modelling or formulating process, however is nor an easy task. There are many problems that nee d to be resolved before such a hybrid system can achieve its goal of h aving the power of both systems. These problems include how to model F PR using a neural network, what necessary modifications to the learnin g algorithm of the neural network need to be done if the neural networ k is to have the same inference mechanism as that of a fuzzy expert sy stem, and where could such a hybrid system be applied. In this paper t he necessary network structure, forward and backward processes of a fu zzy expert network (FEN) used to solve these problems will be presente d. This FEN had been proposed in Tsang and Yeung (1996, World Congress on Neural Networks, pp. 500-503) but whose details in terms of networ k structure, forward and backward reasoning mechanism have not been co vered. Therefore, this paper aims to introduce the concept of how FEN can formulate and model FPRs. One of its applications is to help knowl edge engineers fine-tune knowledge representation parameters such as c ertainty factor of a rule, threshold value of a proposition and member ship values of a fuzzy set. An experiment will also be performed to de monstrate the tuning capability of this FEN. (C) 1997 Elsevier Science Ltd. All rights reserved.