Title :
Regularized least squares support vector fuzzy regression
Author :
Chen, Yongqi ; Zhong, Zhiguang
Author_Institution :
Intell. Sci. & Electromech. Syst. Lab., Ningbo Univ., Ningbo, China
Abstract :
For estimating fuzzy system which is imprecise and represented by fuzzy sets, a regularized least squares support vector fuzzy regression model is proposed. The proposed fuzzy regression model is applying the fuzzy sets principle in weight vector and bias term. Determining the weight vector and the bias term of the proposed fuzzy regression model only requires a single matrix inversion, as against the solution of a complicated quadratic programming problem in previous support vector fuzzy regression model. Numerical examples are given to demonstrate the effectiveness of the proposed fuzzy regression model.
Keywords :
fuzzy set theory; least squares approximations; matrix algebra; regression analysis; support vector machines; fuzzy regression model; fuzzy sets; regularized least squares support vector fuzzy regression; single matrix inversion; weight vector; Electromechanical systems; Fuzzy control; Fuzzy sets; Fuzzy systems; Laboratories; Least squares approximation; Least squares methods; Quadratic programming; Support vector machine classification; Support vector machines; Fuzzy regression; fuzzy sets; matrix inversion; regularized least squares;
Conference_Titel :
Information Management and Engineering (ICIME), 2010 The 2nd IEEE International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-5263-7
Electronic_ISBN :
978-1-4244-5265-1
DOI :
10.1109/ICIME.2010.5477474