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

Kai, Meng Tay and Tze, Ling Jee and Chee, Peng Lim (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, 23 (2-3). pp. 71-92. ISSN 1875-8967 (Online)

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Abstract

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.

Item Type: Article
Additional Information: Universiti Malaysia Sarawak, (UNIMAS)
Uncontrolled Keywords: Fuzzy inference system, similarity reasoning, analogical reasoning, fuzzy rule interpolation, non-linear programming, monotonicity property, failure mode and effect analysis, UNIMAS, Universiti Malaysia Sarawak, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, IPTA, education
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Depositing User: Karen Kornalius
Date Deposited: 22 May 2014 03:47
Last Modified: 24 Mar 2015 00:49
URI: http://ir.unimas.my/id/eprint/2963

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