An improvement to Kóczy and Hirota's interpolative reasoning in sparse fuzzy rule bases

✍ Scribed by Zhi Qiao Wu; Mizumoto Masaharu; Yan Shi

• Publisher: Elsevier Science
• Year: 1996
• Tongue: English
• Weight: 592 KB
• Volume: 15
• Category: Article
• ISSN: 0888-613X
• DOI: 10.1016/s0888-613x(96)00054-0

✦ Synopsis

In sparse fuzzy rule bases, conventional fuzzy reasoning methods cannot reach a proper conclusion. To tackle this problem, K6czy and Hirota have proposed a method called interpolative reasoning. It has been found that by this method the convexity of the reasoning consequence fuzzy set cannot always be retained. In this paper, the authors give a general convex condition for Kdczy and Hirota's method and, starting from this condition, propose an improvement to the method. Firstly, from the given rules in the sparse rule base is constructed a new rule which is near to the antecedent fuzzy set. Then the reasoning is performed with this new rule, based on similarities of fuzzy sets in the antecedent and consequent parts. It is shown that the proposed method maintains the logical interpretation of modus ponens and guarantees the normality and convexity of the reasoning consequence fuzzy set in some classes of fuzzy rules.