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On Constructing Parsimonious Type-2 Fuzzy Logic Systems via Influential Rule Selection

Shang-Ming Zhou, J.M. Garibaldi, R.I. John, F. Chiclana, Shang-ming Zhou Orcid Logo

IEEE Transactions on Fuzzy Systems, Volume: 17, Issue: 3, Pages: 654 - 667

Swansea University Author: Shang-ming Zhou Orcid Logo

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DOI (Published version): 10.1109/TFUZZ.2008.928597

Abstract

Type-2 fuzzy systems are increasing in popularity, and there are many examples of successful applications. While many techniques have been proposed for creating parsimonious type-1 fuzzy systems, there is a lack of such techniques for type-2 systems. The essential problem is to reduce the number of...

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Published in: IEEE Transactions on Fuzzy Systems
ISSN: 1063-6706 1941-0034
Published: IEEE TRANSACTIONS ON FUZZY SYSTEMS 2009
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URI: https://cronfa.swan.ac.uk/Record/cronfa10027
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Abstract: Type-2 fuzzy systems are increasing in popularity, and there are many examples of successful applications. While many techniques have been proposed for creating parsimonious type-1 fuzzy systems, there is a lack of such techniques for type-2 systems. The essential problem is to reduce the number of rules, while maintaining the system's approximation performance. In this paper, four novel indexes for ranking the relative contribution of type-2 fuzzy rules are proposed, which are termed R-values, c-values, ω1 -values, and ω2 -values. The R-values of type-2 fuzzy rules are obtained by applying a QR decomposition pivoting algorithm to the firing strength matrices of the trained fuzzy model. The c-values rank rules based on the effects of rule consequents, while the ω1 -values and ω2 -values consider both the rule-base structure (via firing strength matrices) and the output contribution of fuzzy rule consequents. Two procedures for utilizing these indexes in fuzzy rule selection (termed "forward selection" and "backward elimination") are described. Experiments are presented which demonstrate that by using the proposed methodology, the most influential type-2 fuzzy rules can be effectively retained in order to construct parsimonious type-2 fuzzy models.
College: Faculty of Medicine, Health and Life Sciences
Issue: 3
Start Page: 654
End Page: 667

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