Rule reduction for efficient inferencing in similarity based reasoning
International Journal of Approximate Reasoning
Similarity-based perceptual reasoning for perceptual computing
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Perceptual reasoning for perceptual computing: a similarity-based approach
IEEE Transactions on Fuzzy Systems
Analysis and design of time-variant fuzzy systems based on dynamic fuzzy inference
Computers & Mathematics with Applications
Similarity-based fuzzy reasoning by DNA computing
International Journal of Bio-Inspired Computation
Using special structured fuzzy measure to represent interaction among IF-THEN rules
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Fuzzy Sets and Systems
Fuzzy Sets and Systems
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If the given fact for an antecedent in a fuzzy production rule (FPR) does not match exactly with the antecedent of the rule, the consequent can still be drawn by technique such as fuzzy reasoning. Many existing fuzzy reasoning methods are based on Zadeh's Compositional Rule of Inference (CRI) which requires setting up a fuzzy relation between the antecedent and the consequent part. There are some other fuzzy reasoning methods which do not use Zadeh's CRI. Among them, the similarity-based fuzzy reasoning methods, which make use of the degree of similarity between a given fact and the antecedent of the rule to draw the conclusion, are well known. In this paper, six similarity-based fuzzy reasoning methods are compared and analyzed. Two of them are newly proposed by the authors. The comparisons are two-fold. One is to compare the six reasoning methods in drawing appropriate conclusions for a given set of FPRs. The other is to compare them based on five issues: 1) types of FPR handled by these methods; 2) the complexity of the methods; 3) the accuracy of the conclusion drawn; 4) the accuracy of the similarity measure; and 5) the multi-level reasoning capability. The results have shed some lights on how to select an appropriate fuzzy reasoning method under different environments