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《Transactions of China Electrotechnical Society》 2005-11
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RBF Neural Networks Based on Dynamic Nearest Neighbor-Clustering Algorithm and Its Application in Prediction of MH-Ni Battery Capacity

Zhang Xiuling Song Jianjun (Yanshan University Qinhuangdao 066004 China)  
The method of controlling the RBFNN data centers of the hidden layer is raised in this article based on the feature of the RBFNN. It is the dynamic nearest neighbor-Clustering Algorithm. This algorithm eliminates the factitious factor affecting the choice of data centers in existing algorithms. The RBFNN has the least nodes and high studying speed. A prediction model of MH-Ni battery is cited and simulated with the dynamic RBFNN. The ideal result is made, and a novel method is presented for prediction of MH-Ni battery capacity.
【CateGory Index】: TM911
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