基于改进的BP神经网络模型的雷达网效能评估
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摘要
为拓展雷达网效能评估的内容、改进评估方法和提高评估效率,重构了雷达网效能评估指标体系,给出了各指标的量化模型和方法;针对BP神经网络收敛速度慢、局部极小化问题,提出了利用遗传算法(GA)优化BP神经网络的方法,并采用GA+BP神经网络模型对雷达网部署方案进行了评估优选.仿真结果表明,运用GA算法改进的BP神经网络具有更快的收敛速度和更高的预测精度,可作为雷达网效能量化评估的有效手段,为雷达兵作战筹划提供科学决策依据.
In order to expand the content of radar network effectiveness evaluation and improve the evaluation methods and efficiency, this paper restructures the index system of radar network effectiveness evaluation,and provides the quantitative models and methods of each index. And then the paper proposes that BP neural network is optimized by using genetic algorithm(GA) method to solve BP neural network's slow convergence speed and local optimum. Finally the paper adopts GA+BP neural network model to evaluate and optimize the deployment scheme of radar network. The simulation results show that the BP neural network improved by GA algorithm has faster convergence speed and higher prediction accuracy, and can be used as an effective means to evaluate the effectiveness of radar network, thus providing a scientific decision-making basis for the operational planning of radar troops.
In order to expand the content of radar network effectiveness evaluation and improve the evaluation methods and efficiency, this paper restructures the index system of radar network effectiveness evaluation,and provides the quantitative models and methods of each index. And then the paper proposes that BP neural network is optimized by using genetic algorithm(GA) method to solve BP neural network's slow convergence speed and local optimum. Finally the paper adopts GA+BP neural network model to evaluate and optimize the deployment scheme of radar network. The simulation results show that the BP neural network improved by GA algorithm has faster convergence speed and higher prediction accuracy, and can be used as an effective means to evaluate the effectiveness of radar network, thus providing a scientific decision-making basis for the operational planning of radar troops.
引文
[1]张培珍,杨根源,张杨,等.雷达网效能评估方法研究[J].舰船科学技术,2010,32(3):121-124.
[2]李志猛,徐培德,冉承新,等.武器系统效能评估理论及应用[M].北京:国防工业出版社,2013:20-28.
[3]陈侠,艾宇迪.应用改进神经网络的无人机三维航迹规划[J].电光与控制,2018,25(9):7-11.
[4]胡然,刘东玉,唐忠.基于RBF神经网络的雷达网抗干扰效果评估[J].舰船电子工程,2012,32(9):77-79.
[5]刘春艳,凌建春,寇林元,等.GA-BP神经网络与BP神经网络性能比较[J].中国卫生统计,2013,30(2):173-176.
[6]罗光,肖卫东,贺平.基于遗传神经网络的作战部队指挥信息系统效能评估[J].四川兵工学报,2015,36(8):72-77.
[7]徐斐,谢洲烨,沈伟,等.基于神经网络的分布式雷达抗干扰效能评估方法[J].现代雷达,2015,37(7):4-7.
[8]卞泓斐,杨根源,于磊.基于粗糙集-神经网络的组网雷达作战效能评估[J].四川兵工学报,2015,36(6):87-92.
[9]陈轩.基于遗传算法雷达组网方法研究[D].武汉:华中科技大学,2017:26-32.
[10]袁超鹏.雷达网抗干扰效能评估[D].西安:西安电子科技大学,2014:8-14.
[11]陈永光,李修和,沈阳.组网雷达作战能力分析与评估[M].北京:国防工业出版社,2006:323-325.
[12]段锁力,张多林,宋志华.网络化防空导弹体系跟踪制导网抗干扰能力研究[J].战术导弹技术,2010(4):20-22.
[13]任谢楠.基于遗传算法的BP神经网络的优化研究及Matlab仿真[D].天津:天津师范大学,2014:37-42.
[14]魏全增,陈机林,高强,等.基于遗传优化RBF神经网络的电动负载模拟器控制[J].现代电子技术,2015,38(21):113-117.
[15]张亚昕.基于改进遗传算法的自动组卷问题研究[J].现代电子技术,2012,35(18):80-82.
[2]李志猛,徐培德,冉承新,等.武器系统效能评估理论及应用[M].北京:国防工业出版社,2013:20-28.
[3]陈侠,艾宇迪.应用改进神经网络的无人机三维航迹规划[J].电光与控制,2018,25(9):7-11.
[4]胡然,刘东玉,唐忠.基于RBF神经网络的雷达网抗干扰效果评估[J].舰船电子工程,2012,32(9):77-79.
[5]刘春艳,凌建春,寇林元,等.GA-BP神经网络与BP神经网络性能比较[J].中国卫生统计,2013,30(2):173-176.
[6]罗光,肖卫东,贺平.基于遗传神经网络的作战部队指挥信息系统效能评估[J].四川兵工学报,2015,36(8):72-77.
[7]徐斐,谢洲烨,沈伟,等.基于神经网络的分布式雷达抗干扰效能评估方法[J].现代雷达,2015,37(7):4-7.
[8]卞泓斐,杨根源,于磊.基于粗糙集-神经网络的组网雷达作战效能评估[J].四川兵工学报,2015,36(6):87-92.
[9]陈轩.基于遗传算法雷达组网方法研究[D].武汉:华中科技大学,2017:26-32.
[10]袁超鹏.雷达网抗干扰效能评估[D].西安:西安电子科技大学,2014:8-14.
[11]陈永光,李修和,沈阳.组网雷达作战能力分析与评估[M].北京:国防工业出版社,2006:323-325.
[12]段锁力,张多林,宋志华.网络化防空导弹体系跟踪制导网抗干扰能力研究[J].战术导弹技术,2010(4):20-22.
[13]任谢楠.基于遗传算法的BP神经网络的优化研究及Matlab仿真[D].天津:天津师范大学,2014:37-42.
[14]魏全增,陈机林,高强,等.基于遗传优化RBF神经网络的电动负载模拟器控制[J].现代电子技术,2015,38(21):113-117.
[15]张亚昕.基于改进遗传算法的自动组卷问题研究[J].现代电子技术,2012,35(18):80-82.