Title of article
ADJUSTMENT OF MEMBERSHIP FUNCTIONS, GENERATION AND REDUCTION OF FUZZY RULE BASE FROM NUMERICAL DATA
Author/Authors
Ketata, Raouf National Institute of Applied Science and Technology INSAT, Tunisia , Bellaaj, Hatem National School of Engineers of Sfax - Research unit on Intelligent Control, design and Optimisation of Complex System (ICOS), Tunisia , Chtourou, Mohamed National School of Engineers of Sfax - Research unit on Intelligent Control, design and Optimisation of Complex System (ICOS), TUNISIA , Ben Amer, Mohamed Habib Bourguiba Hospital of Sfax, TUNISIA
From page
147
To page
169
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.
Keywords
fuzzy inference system , rule base generation and reduction , similarity , numerical data.
Journal title
Malaysian Journal of Computer Science
Journal title
Malaysian Journal of Computer Science
Record number
2571860
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