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.

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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
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 089999 Information and Computing Sciences not elsewhere classified
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
Collection: Centre for Intelligent Systems Research
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