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《Chinese Journal of Power Sources》 2012-10
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pplication of relevance vector machine in battery's remaining capacity prediction

QIAO Bo-qiang,HOU Zhen-yi,WANG You-min(Institute of Telecommunication Engineering,Air Force Engineering University,Xi'an Shanxi 710077,China)  
Remaining capacity is an important parameter for battery's administration and control.In order to predict the remaining capacity of VRLA battery accurately and enhance the prediction precision,relevance vector machine was introduced.Compared with LS-SVM and GA-BPNN model,the simulation results show that the proposed method reduce the prediction model's complexity and has higher precision of prediction for practical application.
【CateGory Index】: TM912
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