Refinement of Generated Fuzzy Production Rules by Using a Fuzzy Neural Network

Abstract

Fuzzy production rules (FPRs) have been used for years to capture and represent fuzzy, vague, imprecise and uncertain domain knowledge in many fuzzy systems. There have been a lot of researches on how to generate or obtain FPRs. There exist two methods to obtain FPRs. One is by painstakingly, repeatedly and time-consuming interviewing domain experts to extract the domain knowledge. The other is by using some machine learning techniques to generate and extract FPRs from some training samples. These extracted rules, however, are found to be nonoptimal and sometimes redundant. Furthermore, these generated rules suffer from the problem of low accuracy of classifying or recognizing unseen examples. The reasons for having these problems are 1) the FPRs generated are not powerful enough to represent the domain knowledge, 2) the techniques used to generate FPRs are pre-matured, ad-hoc or may not be suitable for the problem, and 3) further refinement of the extracted rules has not been done. In this paper we look into the solutions of the above problems by 1) enhancing the representation power of FPRs by including local and global weights, 2) developing a fuzzy neural network (FNN) with enhanced learning algorithm, and 3) using this FNN to refine the local and global weights of FPRs. By experimenting our method with some existing benchmark examples, the proposed method is found to have high accuracy in classifying unseen samples without increasing the number of the FPRs extracted and the time required to consult with domain experts is greatly reduced.

Fuzzy knowledge representation and reasoning using a generalized fuzzy petri net and a similarity measure

“…Many fuzzy reasoning methods have been successfully applied in fuzzy expert systems [10,11], especially some similarity-based fuzzy reasoning evaluation methods. A detailed comparison of them can be found in [10].…”

Fuzzy Petri nets Using Intuitionistic Fuzzy Sets and Ordered Weighted Averaging Operators

“…To improve the reasoning accuracy of FPRs, the concept of weights [27], [28] has been incorporated into fuzzy IF-THEN rules, obtaining the WFPRs. Let R = {R 1 , R 2 , .…”

Knowledge representation using fuzzy spiking neural P system

“…After this, fuzzy set theory becomes the base of fuzzy computing systems. On this basis, researchers have combined it with knowledge representation and reasoning methods in artificial intelligence, such as fuzzy production rules [11], fuzzy Petri nets [12], fuzzy neural network [13], fuzzy predicate [14], etc. So far, fuzzy set theory has been widely and maturely used in many areas.…”