An Evolutionary-Based Similarity Reasoning Scheme for Monotonic Multi-Input Fuzzy Inference Systems

Kai, M.T and Chee, P.L (2011) An Evolutionary-Based Similarity Reasoning Scheme for Monotonic Multi-Input Fuzzy Inference Systems. IEEE.

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Abstract

In this paper, an Evolutionary-based Similarity Reasoning (ESR) scheme for preserving the monotonicity property of the multi-input Fuzzy Inference System (FIS) is proposed. Similarity reasoning (SR) is a useful solution for undertaking the incomplete rule base problem in FIS modeling. However, SR may not be a direct solution to designing monotonic multi-input FIS models, owing to the difficulty in getting a set of monotonically-ordered conclusions. The proposed ESR scheme, which is a synthesis of evolutionary computing, sufficient conditions, and SR, provides a useful solution to modeling and preserving the monotonicity property of multi-input FIS models. A case study on Failure Mode and Effect Analysis (FMEA) is used to demonstrate the effectiveness of the proposed ESR scheme in undertaking real world problems that require the monotonicity property of FIS models.

Item Type: Article
Additional Information: Universiti Malaysia Sarawak, (UNIMAS)
Uncontrolled Keywords: Multi-input fuzzy inference system, monotonicity propery, similarity reasoning, fuzzy rule interpolation, UNIMAS, Universiti Malaysia Sarawak, Engineering, university, universiti, Borneo, Malaysia, Sarawak, Kuching, Samarahan, IPTA, education
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Depositing User: Karen Kornalius
Date Deposited: 20 May 2014 02:34
Last Modified: 23 Mar 2015 08:10
URI: http://ir.unimas.my/id/eprint/2733

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