From global weight to fuzzy measure: handling interaction among fuzzy rules

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