Tuning of the structure and parameters of wavelet neural network using improved chaotic PSO

Guangbin Yu; Guixian Li; Yanwei Bai; Xiangyang Jin

doi:10.1109/CHICC.2006.4347595

Tuning of the structure and parameters of wavelet neural network using improved chaotic PSO

Guangbin Yu^*
, Guixian Li
, Yanwei Bai
, Xiangyang Jin

^*Corresponding author for this work

School of Mechatronics Engineering

Harbin Institute of Technology
Harbin University of Commerce

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

This paper presents the tuning of the structure and parameters of a wavelet neural network(WNN) using a improved chaotic particle swarm optimization(ICPSO), the ICPSO approach is a method of combining the improved particle swarm optimization(IPSO), which has a powerful global exploration capability, with the chaotic strategy , which can exploit the local optima. By introduced a new strategy to the ICPSO, it will also be shown that the ICPSO performs better than the traditional PSO and GA based on some benchmark test functions. A WNN with switches introduce to links is proposed. By tuning the structure and improving the connection weights of WNN simultaneously, a partially connected WNN can be obtained. By doing this, it eliminates some ill effects introduced by redundant in features of WNN. An application example on Iris forecasting is given to show the merits of the ICPSO and the improved WNN.

Original language	English
Title of host publication	Proceedings of the 26th Chinese Control Conference, CCC 2007
Pages	228-232
Number of pages	5
DOIs	https://doi.org/10.1109/CHICC.2006.4347595
State	Published - 2007
Event	26th Chinese Control Conference, CCC 2007 - Zhangjiajie, China Duration: 26 Jul 2007 → 31 Jul 2007

Publication series

Name	Proceedings of the 26th Chinese Control Conference, CCC 2007

Conference

Conference	26th Chinese Control Conference, CCC 2007
Country/Territory	China
City	Zhangjiajie
Period	26/07/07 → 31/07/07

Keywords

Chaotic particle swarm optimization
GA
Wavelet neural network

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10.1109/CHICC.2006.4347595

Cite this

@inproceedings{d93d385f155c4a69b4b5abaef38c0c8b,

title = "Tuning of the structure and parameters of wavelet neural network using improved chaotic PSO",

abstract = "This paper presents the tuning of the structure and parameters of a wavelet neural network(WNN) using a improved chaotic particle swarm optimization(ICPSO), the ICPSO approach is a method of combining the improved particle swarm optimization(IPSO), which has a powerful global exploration capability, with the chaotic strategy , which can exploit the local optima. By introduced a new strategy to the ICPSO, it will also be shown that the ICPSO performs better than the traditional PSO and GA based on some benchmark test functions. A WNN with switches introduce to links is proposed. By tuning the structure and improving the connection weights of WNN simultaneously, a partially connected WNN can be obtained. By doing this, it eliminates some ill effects introduced by redundant in features of WNN. An application example on Iris forecasting is given to show the merits of the ICPSO and the improved WNN.",

keywords = "Chaotic particle swarm optimization, GA, Wavelet neural network",

author = "Guangbin Yu and Guixian Li and Yanwei Bai and Xiangyang Jin",

year = "2007",

doi = "10.1109/CHICC.2006.4347595",

language = "英语",

isbn = "7900719229",

series = "Proceedings of the 26th Chinese Control Conference, CCC 2007",

pages = "228--232",

booktitle = "Proceedings of the 26th Chinese Control Conference, CCC 2007",

note = "26th Chinese Control Conference, CCC 2007 ; Conference date: 26-07-2007 Through 31-07-2007",

}

Yu, G, Li, G, Bai, Y & Jin, X 2007, Tuning of the structure and parameters of wavelet neural network using improved chaotic PSO. in Proceedings of the 26th Chinese Control Conference, CCC 2007., 4347595, Proceedings of the 26th Chinese Control Conference, CCC 2007, pp. 228-232, 26th Chinese Control Conference, CCC 2007, Zhangjiajie, China, 26/07/07. https://doi.org/10.1109/CHICC.2006.4347595

Tuning of the structure and parameters of wavelet neural network using improved chaotic PSO. / Yu, Guangbin; Li, Guixian; Bai, Yanwei et al.
Proceedings of the 26th Chinese Control Conference, CCC 2007. 2007. p. 228-232 4347595 (Proceedings of the 26th Chinese Control Conference, CCC 2007).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Tuning of the structure and parameters of wavelet neural network using improved chaotic PSO

AU - Yu, Guangbin

AU - Li, Guixian

AU - Bai, Yanwei

AU - Jin, Xiangyang

PY - 2007

Y1 - 2007

N2 - This paper presents the tuning of the structure and parameters of a wavelet neural network(WNN) using a improved chaotic particle swarm optimization(ICPSO), the ICPSO approach is a method of combining the improved particle swarm optimization(IPSO), which has a powerful global exploration capability, with the chaotic strategy , which can exploit the local optima. By introduced a new strategy to the ICPSO, it will also be shown that the ICPSO performs better than the traditional PSO and GA based on some benchmark test functions. A WNN with switches introduce to links is proposed. By tuning the structure and improving the connection weights of WNN simultaneously, a partially connected WNN can be obtained. By doing this, it eliminates some ill effects introduced by redundant in features of WNN. An application example on Iris forecasting is given to show the merits of the ICPSO and the improved WNN.

AB - This paper presents the tuning of the structure and parameters of a wavelet neural network(WNN) using a improved chaotic particle swarm optimization(ICPSO), the ICPSO approach is a method of combining the improved particle swarm optimization(IPSO), which has a powerful global exploration capability, with the chaotic strategy , which can exploit the local optima. By introduced a new strategy to the ICPSO, it will also be shown that the ICPSO performs better than the traditional PSO and GA based on some benchmark test functions. A WNN with switches introduce to links is proposed. By tuning the structure and improving the connection weights of WNN simultaneously, a partially connected WNN can be obtained. By doing this, it eliminates some ill effects introduced by redundant in features of WNN. An application example on Iris forecasting is given to show the merits of the ICPSO and the improved WNN.

KW - Chaotic particle swarm optimization

KW - GA

KW - Wavelet neural network

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DO - 10.1109/CHICC.2006.4347595

M3 - 会议稿件

AN - SCOPUS:37749049422

SN - 7900719229

SN - 9787900719225

T3 - Proceedings of the 26th Chinese Control Conference, CCC 2007

SP - 228

EP - 232

BT - Proceedings of the 26th Chinese Control Conference, CCC 2007

T2 - 26th Chinese Control Conference, CCC 2007

Y2 - 26 July 2007 through 31 July 2007

ER -

Tuning of the structure and parameters of wavelet neural network using improved chaotic PSO

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