Abstract:
Fuzzy rules generated from neuro-fuzzy systems may contain ambiguous rules, due to numerous factors. While contradiction-correction often ensures consistency in fuzzy rul...Show MoreMetadata
Abstract:
Fuzzy rules generated from neuro-fuzzy systems may contain ambiguous rules, due to numerous factors. While contradiction-correction often ensures consistency in fuzzy rule-bases, a differing approach should be reserved for problems where the linguistic definitions can be mutually-inclusive. For these cases, the proposed ambiguity-correction approach is a simple procedure that prevents excessive skew towards stronger rules, and still creates consistent fuzzy rule-base. This paper describes a proof-of-concept model, ACPOP-CRI(S), where ambiguity-correction can be adapted to the generic POP-CRI(S) framework. Experimental results on the Nakanishi dataset shows that the ACPOP rule identification algorithm has the potential to perform better, and generate fewer rules than the generic POP algorithm.
Published in: 2009 IEEE International Conference on Fuzzy Systems
Date of Conference: 20-24 August 2009
Date Added to IEEE Xplore: 02 October 2009
ISBN Information:
Print ISSN: 1098-7584