Adjusting Membership Functions and Generating TSK Fuzzy Systems from Numerical Data: Application to a Medical Case

Vol.2 No.1

Year: 2007

Issue: Jul-Sep

Title: Adjusting Membership Functions and Generating TSK Fuzzy Systems from Numerical Data: Application to a Medical Case   

Author Name: Hatem Bellaaj, Raouf Ketata, Imed Maaloul, Chtourou M, Mounir Ben Jemaa   

Synopsis:   

This paper presents a new algorithm for generating fuzzy rule base from numerical data. The algorithm is based on different concepts: First, the Mendel Wang generating method for constructing rules premises. Second, the gradient descent method for the identification of Takagi—Sugeno—Kang (TSK) parameters. Third, the similarity measure between fuzzy sets premises and TSK parameters. The principal idea consists in the adjustment of membership function if similarity exists using initial numerical values. The benefits consist in a better fuzzy sets definition without reducing fuzzy rule bases or losing precision. This paper focuses on the application of this approach to the non linear function and a medical problem.