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Journal of Control Science and Engineering
Volume 2017, Article ID 3614790, 12 pages
Research Article

Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function

1College of Electrical and Information Engineering, Quzhou University, Quzhou 324000, China
2Logistics Engineering College, Shanghai Maritime University, Shanghai 200000, China
3Department of Automation, Zhejiang University of Technology, Hangzhou 310023, China

Correspondence should be addressed to Daxing Xu; moc.361@uxgnixad

Received 30 June 2017; Revised 14 September 2017; Accepted 8 October 2017; Published 2 November 2017

Academic Editor: Yuan Yao

Copyright © 2017 Hailun Wang and Daxing Xu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A new model parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function is proposed in this paper. We choose the mixed kernel function as the kernel function of support vector regression. The mixed kernel function of the fusion coefficients, kernel function parameters, and regression parameters are combined together as the parameters of the state vector. Thus, the model selection problem is transformed into a nonlinear system state estimation problem. We use a 5th-degree cubature Kalman filter to estimate the parameters. In this way, we realize the adaptive selection of mixed kernel function weighted coefficients and the kernel parameters, the regression parameters. Compared with a single kernel function, unscented Kalman filter (UKF) support vector regression algorithms, and genetic algorithms, the decision regression function obtained by the proposed method has better generalization ability and higher prediction accuracy.