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A new monotonicity index for fuzzy rule-based systems
conference contribution
posted on 2014-09-04, 00:00 authored by L M Pang, K M Tay, Chee Peng LimChee Peng LimA 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
Event
IEEE International Conference on Fuzzy Systems (2014 : Beijing, China)Pagination
1566 - 1570Publisher
IEEELocation
Beijing, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-07-06End date
2014-07-11ISSN
1098-7584ISBN-13
9781479920723Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2014, Institute of Electrical and Electronics EngineersEditor/Contributor(s)
[Unknown]Title of proceedings
FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy SystemsUsage metrics
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