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. Attached Files Name Description MIMEType Size Downloads Title A new monotonicity index for fuzzy rule-based systems Author(s) Pang,LM Tay,KM 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 Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to Elements publication management system Go to link with your DU access privileges   Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 11 Mar 2015, 14:10:34 EST Thu, 12 Mar 2015, 04:04:39 EST Thu, 12 Mar 2015, 07:13:39 EST Tue, 24 Mar 2015, 15:32:02 EST Tue, 24 Mar 2015, 15:33:16 EST Wed, 25 Mar 2015, 05:09:08 EST Wed, 25 Mar 2015, 10:02:54 EST Wed, 25 Mar 2015, 10:04:18 EST Wed, 25 Mar 2015, 10:12:25 EST Thu, 28 May 2015, 14:22:11 EST Sat, 22 Jan 2022, 01:31:46 EST Filtered Full Citation counts:   Cited 4 times in TR Web of Science Cited 4 times in Scopus Search Google Scholar Access Statistics: 433 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Tue, 24 Mar 2015, 15:33:16 EST