Fuzzy regular least squares twin support vector machine and its application in fault diagnosis

Abstract

Objective function ignores the generalization error in LSTSVM, and training overfitting results in poor generalization performance. All sample labels are considered deterministic, however, some samples contain outliers affected by noises, which leads to low reliability. A recognition method based on fuzzy regular LSTSVM (FRLSTSVM) is proposed. Firstly, L2 norm regular term is introduced into objective function to improve the generalization performance. Secondly, outliers of samples are detected through support vector domain description (SVDD), which improves outlier detection accuracy. Then, a membership degree S3 is constructed to give the outliers a suitable membership degree, reducing the impact of outliers on results. Finally, FRLSTSVM is extended to a multi-classification model by one versus one (OVO) and binary tree (BT) strategies, and it is combined with an improved multiscale fluctuating Rényi dispersion entropy (IMFRDE) for fault diagnosis. The results show that the method has stronger generalization, lower sensitivity to parameters and higher reliability.