Building Fuzzy Inference Systems with Similarity Reasoning: NSGA II-based Fuzzy Rule Selection and Evidential Functions

Tze, Ling Jee and Kok, Chin Chai and Kai, Meng Tay and Chee, Peng Lim (2014) Building Fuzzy Inference Systems with Similarity Reasoning: NSGA II-based Fuzzy Rule Selection and Evidential Functions. In: 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

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Abstract

In our previous investigations, two Similarity Reasoning (SR)-based frameworks for tackling real-world problems have been proposed. In both frameworks, SR is used to deduce unknown fuzzy rules based on similarity of the given and unknown fuzzy rules for building a Fuzzy Inference System (FIS). In this paper, we further extend our previous findings by developing (1) a multi-objective evolutionary model for fuzzy rule selection; and (2) an evidential function to facilitate the use of both frameworks. The Non-Dominated Sorting Genetic Algorithms-II (NSGA-II) is adopted for fuzzy rule selection, in accordance with the Pareto optimal criterion. Besides that, two new evidential functions are developed, whereby given fuzzy rules are considered as evidence. Simulated and benchmark examples are included to demonstrate the applicability of these suggestions. Positive results were obtained.

Item Type: Proceeding (Paper)
Uncontrolled Keywords: Fuzzy Inference System, Non-Dominated Sorting Genetic Algorithms-II, Similarity Reasoning, evidential functions, fuzzy rule selection, Engineering, UNIMAS, 2014, Universiti Malaysia Sarawak
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Academic Faculties, Institutes and Centres > Faculty of Engineering
Depositing User: Karen Kornalius
Date Deposited: 24 Oct 2014 02:05
Last Modified: 11 Mar 2015 04:28
URI: http://ir.unimas.my/id/eprint/5192

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