Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function
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 o...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/3614790 |
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| _version_ | 1850232094036328448 |
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| author | Hailun Wang Daxing Xu |
| author_facet | Hailun Wang Daxing Xu |
| author_sort | Hailun Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0a3c7f97079249e1adc756d62d9ad32d |
| institution | OA Journals |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-0a3c7f97079249e1adc756d62d9ad32d2025-08-20T02:03:17ZengWileyJournal of Control Science and Engineering1687-52491687-52572017-01-01201710.1155/2017/36147903614790Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel FunctionHailun Wang0Daxing Xu1College of Electrical and Information Engineering, Quzhou University, Quzhou 324000, ChinaCollege of Electrical and Information Engineering, Quzhou University, Quzhou 324000, ChinaSupport 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.http://dx.doi.org/10.1155/2017/3614790 |
| spellingShingle | Hailun Wang Daxing Xu Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function Journal of Control Science and Engineering |
| title | Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function |
| title_full | Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function |
| title_fullStr | Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function |
| title_full_unstemmed | Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function |
| title_short | Parameter Selection Method for Support Vector Regression Based on Adaptive Fusion of the Mixed Kernel Function |
| title_sort | parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function |
| url | http://dx.doi.org/10.1155/2017/3614790 |
| work_keys_str_mv | AT hailunwang parameterselectionmethodforsupportvectorregressionbasedonadaptivefusionofthemixedkernelfunction AT daxingxu parameterselectionmethodforsupportvectorregressionbasedonadaptivefusionofthemixedkernelfunction |