Evolutionarily adjusting membership functions in Takagi-Sugeno fuzzy systems. (16th May 2011)

• Record Type: Journal Article
• Authors: Hong, Tzung-Pei; Lin, Wei-Tee; Chen, Chun-Hao; Ouyang, Chen-Sen
• Is Part Of: International journal of intelligent information and database systems. Volume 5:Number 3(2011)
• Page Start: 229
• Page End: 245
• Publication Date: 2011-05-16
• Subjects: genetic algorithms -- GAs -- membership functions -- fuzzy inference -- Takagi-Sugeno fuzzy model -- machine learning -- fuzzy set theory -- fuzzy logic -- TS fuzzy model; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

Abstract

Fuzzy set theory has been used more and more frequently in intelligent systems because of its simplicity and similarity to human reasoning. It usually uses a fuzzy inference system to handle new cases for making decisions or controlling actions. In the past, Takagi and Sugeno proposed a well-known fuzzy model, namely TS fuzzy model, to improve the precision of inference results. In this paper, we try to automatically adjust the membership functions appropriate for the TS fuzzy model. A GA-based learning algorithm is thus proposed to achieve the purpose. The proposed approach considers the shapes of membership functions in fitness evaluation in addition to the accuracy. The experimental results show that the proposed approach can derive the membership functions in the Takagi-Sugeno system with low errors and good shapes.