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A new monotonicity index for fuzzy rule-based systems

Pang,LM, Tay,KM and Lim,CP 2014, A new monotonicity index for fuzzy rule-based systems, in FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 1566-1570, doi: 10.1109/FUZZ-IEEE.2014.6891555.

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Title A new monotonicity index for fuzzy rule-based systems
Author(s) Pang,LM
Tay,KM
Lim,CPORCID iD for Lim,CP orcid.org/0000-0003-4191-9083
Conference name IEEE International Conference on Fuzzy Systems (2014 : Beijing, China)
Conference location Beijing, China
Conference dates 6-11 July 2014
Title of proceedings FUZZ-IEEE 2014 : Proceedings of the 2014 IEEE International Conference on Fuzzy Systems
Editor(s) [Unknown]
Publication date 2014
Conference series IEEE International Conference on Fuzzy Systems
Start page 1566
End page 1570
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Fuzzy inference system
fuzzy rule base
monotonicity index
Summary 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.
ISBN 9781479920723
ISSN 1098-7584
Language eng
DOI 10.1109/FUZZ-IEEE.2014.6891555
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2014, Institute of Electrical and Electronics Engineers
Persistent URL http://hdl.handle.net/10536/DRO/DU:30070583

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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