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基于GM-RBF神经网络的导弹武器系统使用可用度评估方法 研究
引用本文:刘炜,李田科,于仕财,李建. 基于GM-RBF神经网络的导弹武器系统使用可用度评估方法 研究[J]. 装备环境工程, 2013, 10(6): 108-113
作者姓名:刘炜  李田科  于仕财  李建
作者单位:中国人民解放军91980部队,山东烟台264001;中国人民解放军91980部队,山东烟台264001;海军航空工程学院电子信息工程系,山东烟台264001;中国人民解放军91980部队,山东烟台264001
基金项目:国家自然科学基金(61174031)
摘    要:目的研究导弹武器系统使用可用度评估问题,方法通过基于故障数据的使用可用度评估,提出一种基于灰色模型GM(1,1)的径向基函数(RBF—Radial Basis Function)神经网络组合模型。结果该模型克服了灰色理论的长时间序列预估误差大和神经网络的训练样本需求量大、输入变量选取困难等缺点。结论仿真结果表明,GM—RBF神经网络对导弹武器系统使用可用度评估具有评估误差小、精度高等优点。

关 键 词:使用可用度  GM—RBF神经网络  导弹武器系统  评估方法
收稿时间:2013-07-27
修稿时间:2013-08-13

Study on the Operational Availability Evaluation Method of Missile Weapon System Based on GM-RBF Neural Network
LIU Wei,LI Tian-ke,YU Shi-cai and LI Jian. Study on the Operational Availability Evaluation Method of Missile Weapon System Based on GM-RBF Neural Network[J]. Equipment Environmental Engineering, 2013, 10(6): 108-113
Authors:LIU Wei  LI Tian-ke  YU Shi-cai  LI Jian
Affiliation:1. No. 91980 Unit of PLA, Yantai 264001, China; 2. Department of Electronic & Information Engineering, Naval Aeronautical & Astronautical University, Yantai 264001, China)
Abstract:Objective To investigate the operational availability evaluation method of missile weapon system. Methods According to the operational availability evaluation problem of missile weapon system, a combined model was put forward by operational availability evaluation using fault data based on GM (1,1) and RBF neural network. Results The model overcame the disadvantages of big error and long time series prediction of GM (1,1) and great demand of training samples and difficulty of variables selection of neural network. Conclusion Experimental results reveal that the operational availability evaluation of missile weapon system by GM-RBF neural network owns the advantages of evaluation minor error and high precision.
Keywords:operational availability  GM-RBF neural network  missile weapon system  evaluation method
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