An Adaptive Chaotic PSO for Parameter Optimization and Feature Extraction of LS-SVM Based Modelling

Weijian CHENG; Jinliang DING; Weijian KONG; Tianyou CHAI; S. Joe QIN

doi:10.1109/acc.2011.5991217

An Adaptive Chaotic PSO for Parameter Optimization and Feature Extraction of LS-SVM Based Modelling

Weijian CHENG^*, Jinliang DING, Weijian KONG, Tianyou CHAI, S. Joe QIN

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

11 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 2011 American Control Conference
Publisher	Institute of Electrical and Electronics Engineers
Pages	3263-3268
Number of pages	6
ISBN (Electronic)	9781457700811
ISBN (Print)	9781457700804
DOIs	https://doi.org/10.1109/acc.2011.5991217
Publication status	Published - Jun 2011
Externally published	Yes
Event	2011 American Control Conference, ACC 2011 - San Francisco, United States Duration: 29 Jun 2011 → 1 Jul 2011

Publication series

Name	Proceedings of the American Control Conference
Publisher	Institute of Electrical and Electronics Engineers
ISSN (Print)	0743-1619
ISSN (Electronic)	2378-5861

Conference

Conference	2011 American Control Conference, ACC 2011
Country/Territory	United States
City	San Francisco
Period	29/06/11 → 1/07/11

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10.1109/acc.2011.5991217

Cite this

CHENG, W., DING, J., KONG, W., CHAI, T., & QIN, S. J. (2011). An Adaptive Chaotic PSO for Parameter Optimization and Feature Extraction of LS-SVM Based Modelling. In Proceedings of the 2011 American Control Conference (pp. 3263-3268). Article 5991217 (Proceedings of the American Control Conference). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/acc.2011.5991217

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title = "An Adaptive Chaotic PSO for Parameter Optimization and Feature Extraction of LS-SVM Based Modelling",

abstract = "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. {\textcopyright} 2011 AACC American Automatic Control Council.",

author = "Weijian CHENG and Jinliang DING and Weijian KONG and Tianyou CHAI and QIN, {S. Joe}",

year = "2011",

month = jun,

doi = "10.1109/acc.2011.5991217",

language = "English",

isbn = "9781457700804",

series = "Proceedings of the American Control Conference",

publisher = "Institute of Electrical and Electronics Engineers",

pages = "3263--3268",

booktitle = "Proceedings of the 2011 American Control Conference",

note = "2011 American Control Conference, ACC 2011 ; Conference date: 29-06-2011 Through 01-07-2011",

}

CHENG, W, DING, J, KONG, W, CHAI, T & QIN, SJ 2011, An Adaptive Chaotic PSO for Parameter Optimization and Feature Extraction of LS-SVM Based Modelling. in Proceedings of the 2011 American Control Conference., 5991217, Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers, pp. 3263-3268, 2011 American Control Conference, ACC 2011, San Francisco, California, United States, 29/06/11. https://doi.org/10.1109/acc.2011.5991217

An Adaptive Chaotic PSO for Parameter Optimization and Feature Extraction of LS-SVM Based Modelling. / CHENG, Weijian; DING, Jinliang; KONG, Weijian et al.
Proceedings of the 2011 American Control Conference. Institute of Electrical and Electronics Engineers, 2011. p. 3263-3268 5991217 (Proceedings of the American Control Conference).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

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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.

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M3 - Conference paper (refereed)

SN - 9781457700804

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BT - Proceedings of the 2011 American Control Conference

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An Adaptive Chaotic PSO for Parameter Optimization and Feature Extraction of LS-SVM Based Modelling

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