TY - GEN
T1 - An Adaptive Chaotic PSO for Parameter Optimization and Feature Extraction of LS-SVM Based Modelling
AU - Cheng, Weijian
AU - Ding, Jinliang
AU - Kong, Weijian
AU - Chai, Tianyou
AU - Qin, S. Joe
PY - 2011/6
Y1 - 2011/6
N2 - While training an LS-SVM model, two main challenges are parameter optimization and input feature extraction. The main purpose of this article is to address these two problems. Commonly used tools are PSO and BPSO, but they are not suitable for the optimization issues of many local optima owing to its random numbers used to update velocities. In this paper, an adaptive chaotic particle swarm optimization (cPSO) algorithm is proposed to enhance its global searching capability and local searching capability. The practicality of the proposed scheme is demonstrated by application to mineral process for the prediction models between production rate of the concentrated ore and the technical indexes. Compared with the original methods of grid search+PCA, GA+PCA, PSO+PCA as well as PSO+BPSO, the proposed strategy outperforms these existing methods in terms of convergence accuracy. © 2011 AACC American Automatic Control Council.
AB - While training an LS-SVM model, two main challenges are parameter optimization and input feature extraction. The main purpose of this article is to address these two problems. Commonly used tools are PSO and BPSO, but they are not suitable for the optimization issues of many local optima owing to its random numbers used to update velocities. In this paper, an adaptive chaotic particle swarm optimization (cPSO) algorithm is proposed to enhance its global searching capability and local searching capability. The practicality of the proposed scheme is demonstrated by application to mineral process for the prediction models between production rate of the concentrated ore and the technical indexes. Compared with the original methods of grid search+PCA, GA+PCA, PSO+PCA as well as PSO+BPSO, the proposed strategy outperforms these existing methods in terms of convergence accuracy. © 2011 AACC American Automatic Control Council.
UR - https://www.scopus.com/pages/publications/80053151277
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-80053151277&origin=recordpage
U2 - 10.1109/acc.2011.5991217
DO - 10.1109/acc.2011.5991217
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 9781457700804
T3 - Proceedings of the American Control Conference
SP - 3263
EP - 3268
BT - Proceedings of the 2011 American Control Conference
PB - IEEE
T2 - 2011 American Control Conference, ACC 2011
Y2 - 29 June 2011 through 1 July 2011
ER -