Title: Application of adaptive genetic algorithm for the parameter selection of support vector regression

Authors: Hu Zhang; Min Wang; Xinhan Huang; Hubert Roth

Addresses: School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China ' School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China ' School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China ' Department of Electrical Engineering and Computer Science, University of Siegen, Hoelderlinstr. 3, Siegen, D-57068, Germany

Abstract: In order to make an exact prediction for the density of the lead-acid battery electrolyte, this paper proposes a method by using a genetic algorithm to optimise the support vector regression. In this AGA-SVR model, a kind of adaptive genetic algorithm is exploited to choose the model parameters of support vector regression for obtaining better prediction performance. The proposed predicting model is applied to the density predicting for lead-acid battery. The experimental results indicate that both GA and AGA have good efficiency on parameter optimisation. Furthermore, the AGA-SVR model provides a superior prediction performance than the other three models including SPSO-SVR model, IPSO-SVR model and GA-SVR model. Therefore, the AGA method could be considered as an effective alternative method for predicting electrolyte density.

Keywords: support vector regression; SVR; genetic algorithms; parameter optimisation; lead-acid batteries; density prediction; parameter selection; electrolyte density.

DOI: 10.1504/IJMIC.2014.059390

International Journal of Modelling, Identification and Control, 2014 Vol.21 No.1, pp.29 - 37

Published online: 07 Jun 2014 *

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