LTS-SVM learning mechanism with wavelet for modeling of nonlinear systems with noise and outliers

In recently, there are many machine learning approaches have developed for intelligent control. One of these approaches is least squares-support vector machine regression (LS-SVMR). Besides, the robustness problem of the LS-SVMR among machine learning algorithms is importantly considered in recent years. Hence, for the robustness problem in LS-SVMR, a least trimmed squares support vector machine regression (LTS-SVMR) with wavelet kernel which is the hybrid of the LTS and LS-SVMR is proposed in this paper. When the LTS method faces on the training sample with noise and outliers, it can effectively remove large noise and outliers under the proper initial nonlinear function. Hence, robustness problem in LS-SVMR is enhanced by combining the LS-SVMR with wavelet kernel and the LTS. Finally, the proposed LTS-SVMR with wavelet kernel is applied on modeling of nonlinear systems with noise and outliers.