Paper Info
Title
An improvement to Kóczy and Hirota's interpolative reasoning in sparse fuzzy rule bases
Abstract
In sparse fuzzy rule bases, conventional fuzzy reasoning methods cannot reach a proper conclusion. To tackle this problem, Kóczy 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 Kóczy 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.
Year
DOI
Venue
1996
10.1016/S0888-613X(96)00054-0
International Journal of Approximate Reasoning
Keywords
DocType
Volume
approximate reasoning,fuzzy rule interpolation,similarity
Journal
15
Issue
ISSN
Citations 
3
0888-613X
45
PageRank 
References 
Authors
3.15
4
3
Name
Order
Citations
PageRank
Wu Zhi Qiao117017.47
Masaharu Mizumoto2766406.85
Yan Shi328527.64