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A new monotonicity index for fuzzy rule-based systems

Version 2 2024-06-06, 08:05
Version 1 2015-03-11, 15:10
conference contribution
posted on 2024-06-06, 08:05 authored by LM Pang, KM Tay, Chee Peng Lim
A search in the literature reveals that mathematical conditions (usually sufficient conditions) for the Fuzzy Inference System (FIS) models to satisfy the monotonicity property have been developed. A monotonically-ordered fuzzy rule base is important to maintain the monotonicity property of an FIS. However, it may difficult to obtain a monotonically-ordered fuzzy rule base in practice. We have previously introduced the idea of fuzzy rule relabeling to tackle this problem. In this paper, we further propose a monotonicity index for the FIS system, which serves as a metric to indicate the degree of a fuzzy rule base fulfilling the monotonicity property. The index is useful to provide an indication whether a fuzzy rule base should (or should not) be used in practice, even with fuzzy rule relabeling. To illustrate the idea, the zero-order Sugeno FIS model is exemplified. We add noise as errors into the fuzzy rule base to formulate a set of non-monotone fuzzy rules. As such, the metric also acts as a measure of noise in the fuzzy rule base. The results show that the proposed metric is useful to indicate the degree of a fuzzy rule base fulfilling the monotonicity property.

History

Pagination

1566-1570

Location

Beijing, China

Start date

2014-07-06

End date

2014-07-11

ISSN

1098-7584

ISBN-13

9781479920723

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2014, Institute of Electrical and Electronics Engineers

Editor/Contributor(s)

[Unknown]

Title of proceedings

FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems

Event

IEEE International Conference on Fuzzy Systems (2014 : Beijing, China)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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