Least squares support vector machine(LS-SVM) is computationally more effi cient than the standard SVM, but unfortunately the robustness of standard SVM is lost. LS-SVM might lead to estimates which are less robust with respect to outliers on the data or when the assumption of a Gaussian distribution for error variables is not realistic. Therefore, an approach based on the robust least squares support vector machine(RLS-SVM) is proposed, in which robust learning algorithm(RLA) is employed to enhance the robust capability of LS-SVM. Finally, simulation analysis and the modeling of a typical plant for hydrometallurgy illustrate the effectiveness and feasibility of the presented method.

基于鲁棒学习的最小二乘支持向量机及其应用.pdf

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