LTS-SVMR for Modeling of Nonlinear Systems with Noise and Outliers
In general, there are many machine learning algorithms have developed for the intelligent control. Besides, support vector machine (SVM) among machine learning is generally used in recent years. That is, many contributions about the support vector machine regression (SVMR) and the least squares-support vector machine regression (LS-SVMR) can be found in some well-known journals. In this paper, for the robustness problem of LS-SVMR, we propose the least trimmed squares support vector machine regression (LTS-SVMR) which is the hybrid of the least trimmed squares (LTS). That is, 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. That is, robustness of the LS-SVMR is enhanced by combining the LS-SVMR and the LTS. Finally, the proposed LTS-SVMR is applied on modeling of nonlinear systems with noise and outliers.
KeywordsLeast squares support vector machine regression least trimmed squares modeling noise and outliers
Unable to display preview. Download preview PDF.
- 1.Karatzoglou, A., Meyer, D.: Support Vector Machines in R. Journal of Statistical Software 15(9), 1–28 (2006)Google Scholar
- 2.Chuang, C.C., Lai, M.H., Chen, S.S., Jeng, J.T.: Hybrid robust LS-SVMR with outliers for MIMO system. In: IEEE International Conference on Systems Man and Cybernetics (SMC), pp. 10–13 (October 2010)Google Scholar
- 5.Camps-Valls, G., Soria-Olivas, E., Perez-Ruixo, J.J., Perez-Cruz, F., Artes-Rodriguez, A., Jimenez-Torres, N.V.: Therapeutic Drug Monitoring of Kidney Transplant Recipients Using Profiled Support Vector Machines. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Aplications and Reviewa 37(3), 359–372 (2007)CrossRefGoogle Scholar
- 6.Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Vandewalle, J.: Least Squares Support Vector Machines, ch. 2. World Scientific, New Jersey (2002)Google Scholar
- 7.Valyon, J., Horvath, G.: A Weighted Generalized LS–SVM. Periodica Polytechnica Ser. El. Eng. 47(3), 229–251 (2003)Google Scholar
- 9.Rousseeuw, P.J., Leroy, A.M.: Robust Regression and outlier Detection. John Wiley & Sons, Inc., Hoboken (2003)Google Scholar
- 11.Alpaydm, E.: Introduction to Machine Learning, ch.1. The MIT Press (October 2004)Google Scholar