郑州轻工业学院学报（自然科学版）2012，Vol.27Issue(4)：12-15,4.DOI:10.3969/j.issn.1004-1478.2012.04.004
基于BP神经网络的阀控铅酸盐蓄电池劣化程度预测
Impairment degree forecast for valve regulated lead acid battery based on BP neural network
摘要
Abstract
In order to improve forecast accurancy of impairment degree for valve regulated lead acid battery,a forecast model based on neural network with autonomic learning function was structured. The BP neural network was trained and learned using 192 different discharge degree data,then the real-time collection data were forecasted and analyzed using trained BP neural network. The forecast accurancy is above 93% ,which proves the forecast model' s validity.关键词
阀控铅酸盐蓄电池/BP神经网络/劣化程度预测Key words
valve regulated lead acid battery/ BP neural network/ impairment degree forecast分类
信息技术与安全科学引用本文复制引用
李东玉,王睿,冯宜民..基于BP神经网络的阀控铅酸盐蓄电池劣化程度预测[J].郑州轻工业学院学报（自然科学版）,2012,27(4):12-15,4.基金项目
河南省电力公司科技攻关项目(豫电科20111613) （豫电科20111613）
