Fuzzy fault diagnosis based on fuzzy robust v-support vector classifier and modified genetic algorithm

Abstract

This paper presents a new version of fuzzy support vector classifier machine (SVM) which can penalize those hybrid noises to forecast fuzzy nonlinear system. Since there exist some problems of uncertain data in many actual forecasting problem, the input variables are described as fuzzy numbers by fuzzy comprehensive evaluation. To solve the shortage of ε-insensitive loss function for hybrid noises such as singularity points, biggish magnitude noises and Gaussian noises, a novel robust loss function is proposed in this paper. Then by the integration of the triangular fuzzy theory, v-SVC and robust loss function theory, fuzzy robust v-SVC (FRv-SVM) which can penalize those hybrid noises is proposed. To seek the optimal parameters of FRv-SVC, genetic algorithm is also proposed to optimize the unknown parameters of FRv-SVC. The results of the application in fuzzy car assembly line system diagnosis confirm the feasibility and the validity of the FRv-SVC model. Compared with other SVC methods, FRv-SVC method has better classifier precison for small sample with hybrid noises.