ACPOP: ambiguity correction-based pseudo-outer-product fuzzy rule identification algorithm

Authors:
Javan Tan;Chai Quek
Affiliations:
Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore;Centre for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Singapore
Venue:
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Year:
2009

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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.