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Parameter selection of support vector regression based on hybrid optimization algorithm and its application

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Abstract

Choosing optimal parameters for support vector regression (SVR) is an important step in SVR design, which strongly affects the performance of SVR. In this paper, based on the analysis of influence of SVR parameters on generalization error, a new approach with two steps is proposed for selecting SVR parameters. First the kernel function and SVM parameters are optimized roughly through genetic algorithm, then the kernel parameter is finely adjusted by local linear search. This approach has been successfully applied to the prediction model of the sulfur content in hot metal. The experiment results show that the proposed approach can yield better generalization performance of SVR than other methods.

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Authors and Affiliations

  1. School of Information Science & Engineering, Central South University, 410083, Changsha, Hunan, China

    Xin Wang, Chunhua Yang, Bin Qin & Weihua Gui

  2. Department of Electric Engineering, Zhuzhou Institute of Technology, 412008, Zhuzhou, Hunan, China

    Xin Wang & Bin Qin

Authors
  1. Xin Wang
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  2. Chunhua Yang
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  3. Bin Qin
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  4. Weihua Gui
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Additional information

This work was supported by the National Natural Science Foundation of China (No. 60574030), the National Basic Research program of China (No. 2002CB312203) and Foundation of Educational Department of Hunan Province (No.05C523) .

Xin WANG received her M.Eng. in Computer Technology and its Application from Central South Forestry University. Currently, she is a Ph. D. candidate in the School of Information Science & Engineering, Central South University. Her research area centers on artificial intelligence methods such as support vector machines, neural networks, and multi-objective optimization. E-mail: wwangxin97@163.com.

Chunhua YANG received her M.Eng. in Automatic Control Engineering and her Ph. D. in Control Science and Engineering from Central South University, China in 1988 and 2002 respectively, and was with the Electrical Engineering Department, Katholieke Universiteit Leuven, Belgium from 1999 to 2001. She is currently a full professor in the School of Information Science & Engineering, Central South University. Her research interests include modeling and optimal control of complex industrial process, intelligent control system, and fault-tolerant computing of real-time systems.

Bin QIN received his M. Eng. in Automatic Control Engineering from Central South University in 1988. Currently, he is a full professor in the Electrical Engineering Department of ZhuZhou Institute of Technology. His research interests include the modeling and intelligent control of complex industries process, application of multi-agent system, and evolutionary algorithms.

Weihua GUI received the the B.Eng. degree (Control Science and Engineering) and the M.Eng. degree (Control Science and Engineering) from Central South University, Changsha, China in 1976 and 1981, respectively. From 1986 to 1988 he was a visiting scholar at Universitat-GH-Duisburg, Germany. He has been a full professor in the School of Information Science & Engineering, Central South University, Changsha, China, since 1991. His main research interests are in modeling and optimal control of complex industrial process, distributed robust control, and fault diagnoses.

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Wang, X., Yang, C., Qin, B. et al. Parameter selection of support vector regression based on hybrid optimization algorithm and its application. J. Control Theory Appl. 3, 371–376 (2005). https://doi.org/10.1007/s11768-005-0026-1

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  • DOI: https://doi.org/10.1007/s11768-005-0026-1

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