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Title: From global weight to fuzzy measure : handling interaction among fuzzy rules
Authors: Yeung, DS
Lee, JWT
Ha, MH
Keywords: Fuzzy set theory
Inference mechanisms
Knowledge representation
Uncertainty handling
Issue Date: 2001
Publisher: IEEE
Source: 2001 IEEE International Conference on Systems, Man, and Cybernetics, 7-10 October 2001, Tucson, AZ, v. 3, p. 1491-1496 How to cite?
Abstract: Global weight is one of the knowledge representation parameters which is assigned to a set of fuzzy production rules for improving the representation accuracy and reducing the occurrence of incorrect inferences of a fuzzy production rule. Due to the inherent interaction among the rules, the fuzzy inferencing mechanism involving global weights performs unsatisfactorily. To handle this interaction, the paper proposes the use of a fuzzy measure (or in general a nonnegative and nonadditive set function) to replace global weights. Such replacement can effectively improve the reasoning results. An initial experimental result shows that, by learning the fuzzy measure, the reasoning accuracy can be improved significantly
ISBN: 0-7803-7087-2
ISSN: 1062-922X
DOI: 10.1109/ICSMC.2001.973494
Appears in Collections:Conference Paper

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