Model selection of support vector regression using particle swarm optimization algorithm

被引:0
作者
Yang, HZ [1 ]
Shao, XG [1 ]
Chen, G [1 ]
Ding, F [1 ]
机构
[1] So Yangtze Univ, Res Ctr Control Sci & Engn, Wuxi 214122, Peoples R China
来源
DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES A-MATHEMATICAL ANALYSIS | 2006年 / 13卷
关键词
support vector regression; model selection; particle swarm optimization; soft-sensor;
D O I
暂无
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
A new automatic search methodology for model selection of support vector regression (SVR), based on the particle swarm optimization (PSO) algorithm, was proposed to search for the adequate hyper-parameters of SVR. In this method, each particle indicates a group of hyper-parameters, and the population is a collection of particles. Two artificial data experiments results show that our method performs Superiorly oil function approximation. Furthermore, the proposed method was applied to establish a soft-sensor model for average molecular weight in polyacrylonitrile productive process. The results of real data simulation also show that this method is effective.
引用
收藏
页码:1417 / 1425
页数:9
相关论文
共 10 条
[1]  
[Anonymous], NC2TR1998030
[2]   Choosing multiple parameters for support vector machines [J].
Chapelle, O ;
Vapnik, V ;
Bousquet, O ;
Mukherjee, S .
MACHINE LEARNING, 2002, 46 (1-3) :131-159
[3]  
Chen PW, 2004, IEEE IJCNN, P2035
[4]  
Cherkassky V, 1997, IEEE Trans Neural Netw, V8, P1564, DOI 10.1109/TNN.1997.641482
[5]  
Eberhart R, 1995, MHS 95 P 6 INT S MIC, P39, DOI DOI 10.1109/MHS.1995.494215
[6]   Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms [J].
Keerthi, SS .
IEEE TRANSACTIONS ON NEURAL NETWORKS, 2002, 13 (05) :1225-1229
[7]  
Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/icnn.1995.488968
[8]  
SHAO XG, 2004, P ANN C AUT JIIANGS
[9]  
YANG HZ, 2002, CONTROL INSTRUMENTS, V29, P11
[10]  
ZHENG CH, 2004, P 5 WORLD C INT CONT