A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems

Tay, Kai Meng, Jee, Tze Ling and Lim, Chee Peng 2012, A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems, Journal of intelligent and fuzzy systems, vol. 23, no. 2-3, pp. 71-92.

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Title A non-linear programming-based similarity reasoning scheme for modelling of monotonicity-preserving multi-input fuzzy inference systems
Author(s) Tay, Kai Meng
Jee, Tze Ling
Lim, Chee Peng
Journal name Journal of intelligent and fuzzy systems
Volume number 23
Issue number 2-3
Start page 71
End page 92
Total pages 22
Publisher IOS Press
Place of publication Amsterdam, The Netherlands
Publication date 2012
ISSN 1064-1246
1875-8967
Keyword(s) analogical reasoning
failure mode and effect analysis
Fuzzy inference system
fuzzy rule interpolation
monotonicity property
non-linear programming
similarity reasoning
Summary In this paper, the zero-order Sugeno Fuzzy Inference System (FIS) that preserves the monotonicity property is studied. The sufficient conditions for the zero-order Sugeno FIS model to satisfy the monotonicity property are exploited as a set of useful governing equations to facilitate the FIS modelling process. The sufficient conditions suggest a fuzzy partition (at the rule antecedent part) and a monotonically-ordered rule base (at the rule consequent part) that can preserve the monotonicity property. The investigation focuses on the use of two Similarity Reasoning (SR)-based methods, i.e., Analogical Reasoning (AR) and Fuzzy Rule Interpolation (FRI), to deduce each conclusion separately. It is shown that AR and FRI may not be a direct solution to modelling of a multi-input FIS model that fulfils the monotonicity property, owing to the difficulty in getting a set of monotonically-ordered conclusions. As such, a Non-Linear Programming (NLP)-based SR scheme for constructing a monotonicity-preserving multi-input FIS model is proposed. In the proposed scheme, AR or FRI is first used to predict the rule conclusion of each observation. Then, a search algorithm is adopted to look for a set of consequents with minimized root means square errors as compared with the predicted conclusions. A constraint imposed by the sufficient conditions is also included in the search process. Applicability of the proposed scheme to undertaking fuzzy Failure Mode and Effect Analysis (FMEA) tasks is demonstrated. The results indicate that the proposed NLP-based SR scheme is useful for preserving the monotonicity property for building a multi-input FIS model with an incomplete rule base.
Language eng
Field of Research 099999 Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2012, IOS Press
Persistent URL http://hdl.handle.net/10536/DRO/DU:30048108

Document type: Journal Article
Collection: Institute for Frontier Materials
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