Building monotonicity-preserving fuzzy inference models with optimization-based similarity reasoning and a monotonicity index

Tay, Kai Meng, Lim, Chee Peng and Jee, Tze Ling 2012, Building monotonicity-preserving fuzzy inference models with optimization-based similarity reasoning and a monotonicity index, in FUZZ-IEEE 2012 : Proceedings of the IEEE 2012 International Conference on Fuzzy Systems, IEEE, [Piscataway, N. J.], pp. 1-8.

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Title Building monotonicity-preserving fuzzy inference models with optimization-based similarity reasoning and a monotonicity index
Author(s) Tay, Kai Meng
Lim, Chee Peng
Jee, Tze Ling
Conference name International Conference on Fuzzy Systems (2012 : Brisbane, Qld.)
Conference location Brisbane, Qld.
Conference dates 10-15 Jun. 2012
Title of proceedings FUZZ-IEEE 2012 : Proceedings of the IEEE 2012 International Conference on Fuzzy Systems
Editor(s) [unknown]
Publication date 2012
Conference series International Conference on Fuzzy Systems
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication [Piscataway, N. J.]
Keyword(s) fuzzy inference system
monotonicity property
local monotonicity
monotonicity index
sufficient conditions
similarity reasoning
Summary In this paper, a novel approach to building a Fuzzy Inference System (FIS) that preserves the monotonicity property is proposed. A new fuzzy re-labeling technique to re-label the consequents of fuzzy rules in the database (before the Similarity Reasoning process) and a monotonicity index for use in FIS modeling are introduced. The proposed approach is able to overcome several restrictions in our previous work that uses mathematical conditions in building monotonicity-preserving FIS models. Here, we show that the proposed approach is applicable to different FIS models, which include the zero-order Sugeno FIS and Mamdani models. Besides, the proposed approach can be extended to undertake problems related to the local monotonicity property of FIS models. A number of examples to demonstrate the usefulness of the proposed approach are presented. The results indicate the usefulness of the proposed approach in constructing monotonicity-preserving FIS models.
ISBN 9781467315050
9781467315074
ISSN 1098-7584
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 E1.1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048733

Document type: Conference Paper
Collection: Institute for Frontier Materials
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