Application of adaptive genetic algorithm for the parameter selection of support vector regression
by Hu Zhang; Min Wang; Xinhan Huang; Hubert Roth
International Journal of Modelling, Identification and Control (IJMIC), Vol. 21, No. 1, 2014

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.

Online publication date: Sat, 07-Jun-2014

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