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基于BP神经网络的野战给水装备性能评估模型
引用本文:于德浩,邓正栋,聂永平,韩宇.基于BP神经网络的野战给水装备性能评估模型[J].解放军理工大学学报,2006,7(5):476-479.
作者姓名:于德浩  邓正栋  聂永平  韩宇
作者单位:解放军理工大学,工程兵工程学院,江苏,南京,210007;解放军理工大学,工程兵工程学院,江苏,南京,210007;解放军理工大学,工程兵工程学院,江苏,南京,210007;解放军理工大学,工程兵工程学院,江苏,南京,210007
摘    要:为了评估野战给水装备的性能,方便部队选购最佳的净水器材,在前人研究的基础上,结合战场需求和当前水质净化技术研究的成果,提出了野战给水装备的指标体系,并利用BP(back-propagation)神经网络对野战给水装备的性能进行了建模,给出了一种新的算法。算例通过对5种野战给水装备的评价结果与专家们的评价结果相比较的方法,验证了模型的可靠性和可行性。得出了该模型能够作为“计算机评价专家”来代替人的评价的结论。可以将此模型应用到部队选购给水装备的实际工作中,方便采购人员对野战给水装备的性能进行横向比较,选出综合性能最高的装备。

关 键 词:BP神经网络  给水装备  指标体系  评估模型
文章编号:1009-3443(2006)05-0476-04
收稿时间:2006-05-17
修稿时间:2006年5月17日

Appraisal model of performance to field waterworks based on BP neural networks
YU De-hao,DENG Zheng-dong,NIE Yong-ping and HAN Yu.Appraisal model of performance to field waterworks based on BP neural networks[J].Journal of PLA University of Science and Technology(Natural Science Edition),2006,7(5):476-479.
Authors:YU De-hao  DENG Zheng-dong  NIE Yong-ping and HAN Yu
Institution:Engineering Institute of Corps of Engineers,PLA Univ.of Sci.& Tech.,Nanjing 210007,China;Engineering Institute of Corps of Engineers,PLA Univ.of Sci.& Tech.,Nanjing 210007,China;Engineering Institute of Corps of Engineers,PLA Univ.of Sci.& Tech.,Nanjing 210007,China;Engineering Institute of Corps of Engineers,PLA Univ.of Sci.& Tech.,Nanjing 210007,China
Abstract:Based on the researches of predecessors and in veiw of the needs of war and technology of purification,an index system of filed waterworks was offered and the appraisal model built with neural networks and a new algorithm presented in order to appraise the performance of field waterworks,and choose and purchase the best watercleaner conveniently.The appraisal results of five waterworks compared with those of the specialists prove that the model is credible and practical.This model can replace man and work as a computer-specialist. So this model can be used in purchasing waterworks for our troops, which makes buyers analyze and compare waterworks conveniently and choose good waterworks.
Keywords:back-propagation neural network  waterworks  index system  appraisal model
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