Interval-based and fuzzy set-based approaches to modeling of fuzzy inference systems with the local monotonicity property Teh, Chin Ying, Tay, Kai Meng and Lim, Chee Peng 2013, Interval-based and fuzzy set-based approaches to modeling of fuzzy inference systems with the local monotonicity property, in FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 1-7. Attached Files Name Description MIMEType Size Downloads Title Interval-based and fuzzy set-based approaches to modeling of fuzzy inference systems with the local monotonicity property Author(s) Teh, Chin Ying Tay, Kai Meng Lim, Chee Peng orcid.org/0000-0003-4191-9083 Conference name Fuzzy Systems. IEEE International Conference (2013 : Hyderabad, India) Conference location Hyderabad, India Conference dates 7-10 Jul. 2013 Title of proceedings FUZZ-IEEE 2013 : Proceedings of the IEEE International Conference on Fuzzy Systems Editor(s) [Unknown] Publication date 2013 Conference series IEEE International Conference on Fuzzy Systems Start page 1 End page 7 Total pages 7 Publisher IEEE Place of publication Piscataway, N.J. Keyword(s) fuzzy inference system local monotonicity monotonicity test interval approach fuzzy set approach datadriven modeling Summary Even though the importance of the local monotonicity property for function approximation problems is well established, there are relative few investigations addressing issues related to the fulfillment of the local monotonicity property in Fuzzy Inference System (FIS) modeling. We have previously conducted a preliminary study on the local monotonicity property of FIS models, with the assumption that the extrema point(s) (i.e., the maximum and/or minimum point(s)) is either known precisely or totally unknown. However, in some practical situations, the extrema point(s) can be known imprecisely (as an interval or a fuzzy set). In this paper, the imprecise information is exploited to construct an FIS model that fulfills the local monotonicity property. A procedure to estimate the extrema point(s) of a function is devised. Applicability of the findings to a datadriven modeling problem is further demonstrated. Language eng Field of Research 080108 Neural, Evolutionary and Fuzzy Computation Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category E1 Full written paper - refereed Copyright notice ©2013, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30057154 Document type: Conference Paper Collections: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research   Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 23 Oct 2013, 10:01:02 EST Wed, 23 Oct 2013, 14:41:04 EST Fri, 01 Nov 2013, 12:42:57 EST Tue, 03 Dec 2013, 15:37:58 EST Wed, 04 Dec 2013, 07:39:28 EST Wed, 04 Dec 2013, 07:40:36 EST Thu, 24 Apr 2014, 12:41:38 EST Thu, 24 Apr 2014, 14:22:05 EST Wed, 21 Oct 2015, 14:52:17 EST Sat, 22 Jan 2022, 01:32:07 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 472 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Wed, 23 Oct 2013, 10:01:02 EST