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

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 Re & PRIME;nyi dispersion entropy (IMFRDE) for fault diagnosis. The results show that the method has stronger generalization, lower sensitivity to parameters and higher reliability.

Automated summary

In this paper, a recognition method based on fuzzy regular LSTSVM (FRLSTSVM) is proposed. L2 norm regular term is introduced to improve generalization performance, and support vector domain description (SVDD) is used to detect outliers. A membership degree S3 is constructed to reduce the impact of outliers on results. The method is also extended to a multi-classification model and combined with an improved multiscale fluctuating Rényi dispersion entropy (IMFRDE) for fault diagnosis.