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Adjustment of Membership Functions, Generation and Reduction of Fuzzy Rule Base From Numerical Data

Ketata, Raouf, and Bellaaj, Hatem, and Mohamed Chtourou, and Amer, Mohamed Ben, (2007) Adjustment of Membership Functions, Generation and Reduction of Fuzzy Rule Base From Numerical Data. Malaysian Journal of Computer Science, 20 (2). ISSN 0127-9084

Full text not available from this repository.

Official URL: http://ejum.fsktm.um.edu.my/ArticleInformation.aspx?ArticleID=551

Affiliations

National Institute of Applied Sciences and Technologies of Tunis, Tunisia
National School of Engineers of Sfax. Research unit on Intelligent Control, design and Optimisation of Complex System
National School of Engineers of Sfax. Research unit on Intelligent Control, design and Optimisation of Complex System
Habib Bourguiba Hospital of Sfax, TUNISIA

Abstract

In this paper we introduce a new approach for adjustment of membership functions, generation, and reduction of fuzzy rule base from data in the same time. The proposed approach consists of five steps: First, generate fuzzy rules from data using Mendel & Wang Method introduced in [1]. Second, calculate the degree of similarity between rules. Third, measure the distance between the numerical values which induces similar rules. Four, if the distance is greater than base value then merge membership functions. Finally, regenerate rules from data with new fuzzy sets. This approach is applied to truck backer-upper control and Liver trauma diagnostic. A comparative study with a simple Mendel Wang method shows the advantages of the developed approach.

Item Type:Journal
Keywords:fuzzy inference system, rule base generation and reduction, similarity, numerical data
Subjects:Q Science
T Technology
ID Code:1613

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