基于PSO-RBF神经网络的海战场电磁态势预测
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摘要
针对海战场电磁态势的预测问题,提出一种基于改进粒子群(PSO)优化径向基函数(RBF)神经网络的海战场电磁态势预测方法。该方法使用自适应惯性权重、模拟退火法和遗传算法对常规的粒子群算法进行改进,提高算法的搜寻精度和速度,并采用改进粒子群算法优化RBF神经网络参数,提高网络的学习效率和预测精度。最后,对海战场电磁态势值之间的非线性映射关系进行仿真预测。实验结果表明,该方法可以有效地提高海战场电磁态势的预测精度,具有较好的适用性。
A sea battlefield electromagnetic state prediction method based on improved particle swarm optimization(PSO)algorithm optimizing radial basis function(RBF) neural network is proposed to solve the prediction problem of sea battlefield electromagnetic state. The adaptive inertia weight,simulated annealing method and genetic algorithm are used in the method to improve the conventional PSO algorithm,and its search accuracy and speed. The improved PSO algorithm is used to optimize the parameters of RBF neural network,which can improve the learning efficiency and prediction accuracy of the network. The simulation prediction is carried out for the non-linear mapping relationship between the electromagnetic state values of the sea battlefield. The experimental results show that the method can improve the prediction accuracy of the sea battlefield electromagnetic state effectively,and has strong applicability.
A sea battlefield electromagnetic state prediction method based on improved particle swarm optimization(PSO)algorithm optimizing radial basis function(RBF) neural network is proposed to solve the prediction problem of sea battlefield electromagnetic state. The adaptive inertia weight,simulated annealing method and genetic algorithm are used in the method to improve the conventional PSO algorithm,and its search accuracy and speed. The improved PSO algorithm is used to optimize the parameters of RBF neural network,which can improve the learning efficiency and prediction accuracy of the network. The simulation prediction is carried out for the non-linear mapping relationship between the electromagnetic state values of the sea battlefield. The experimental results show that the method can improve the prediction accuracy of the sea battlefield electromagnetic state effectively,and has strong applicability.
引文
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[2] TANG D,HAN H,YUAN K. Research on the essence and visualization description method of battlefield electromagnetic environment[J]. Ordnance industry automation,2014(11):57-59.
[3] WANG F,HAN H,WANG J,et al. The complexity evaluation method of electromagnetic environment based on statistical characteristics analysis[J]. Applied mechanics&materials,2013,321/324:779-784.
[4]陈求,戎华,谭亮亮.基于最小二乘法的雷达辐射源精确识别指标权重确定方法[J].舰船电子对抗,2016,39(4):52-55.CHEN Qiu,RONG Hua,TAN Liangliang. A method for determining precise identification indexes of radar emitters based on least squares[J]. Shipboard electronic countermeasure,2016,39(4):52-55.
[5]冯德俊,朱江,李方伟.战场电磁态势感知关键技术研究[J].数字通信,2013,40(5):20-23.FENG Dejun,ZHU Jiang,LI Fangwei. Research on key technologies of electromagnetic situation sensing in battlefield[J].Digital communication,2013,40(5):20-23.
[6] HAN H G,QIAO J F. Prediction of activated sludge bulking based on a self-organizing RBF neural network[J]. Journal of process control,2012,22(6):1103-1112.
[7]杜刚,何朔,于海鹏.基于径向基函数神经网络的空间碎片撞击模式识别研究[J].航天器环境工程,2015,32(4):357-360.DU Gang,HE Shuo,YU Haipeng. Research on pattern recognition of space debris impact based on radial basis function neural network[J]. Spacecraft environment engineering,2015,32(4):357-360.
[8]李方伟,郑波,朱江,等.一种基于AC-RBF神经网络的网络安全态势预测方法[J].重庆邮电大学学报(自然科学版),2014,26(5):576-581.LI Fangwei,ZHENG Bo,ZHU Jiang,et al. An approach to forecast the network security situation based on AC-RBF neural network[J]. Journal of Chongqing University of Posts and Telecommunications(natural science edition),2014,26(5):576-581.
[9]夏轩,许伟明.改进的粒子群算法对RBF神经网络的优化[J].计算机工程与应用,2012,48(5):37-40.XIA Xuan,XU Weiming. Optimization of RBF neural network based on improved particle swarm optimization[J]. Journal of computer engineering and applications,2012,48(5):37-40.
[10] CAI X F,SONG J S. Analysis of complexity in battlefield electromagnetic environment[C]//2009 IEEE Conference on Industrial Electronics and Applications. Xi’an, China:IEEE,2009:2440-2442.
[11]高波,马向玲,隋江波.海战复杂电磁环境分析[J].火力与指挥控制,2013,38(3):1-4.GAO Bo,MA Xiangling,SUI Jiangbo. Analysis of complex electromagnetic environment in sea battle[J]. Fire control&command control,2013,38(3):1-4.
[12]张义,田爱奎,韩士元.一种自适应的混沌粒子群优化RBF神经网络算法[J].重庆理工大学学报,2015,29(11):126-130.ZHANG Yi,TIAN Aikui,HAN Shiyuan. An adaptive RBF neural network algorithm based on chaotic particle swarm optimization[J]. Journal of Chongqing Institute of Technology,2015,29(11):126-130.
[13] CHEN G C,YU J S. Particle swarm optimization algorithm[J]. Information&control,2005,306:1369-1372.
[14]董俊,洪丽娜,汪连栋,等.多辐射平台运动区域电磁环境预测方法[J].现代防御技术,2016,44(2):190-196.DONG Jun,HONG Lina,WANG Liandong,et al. An electromagnetic environment prediction method in multi-radiation platform motion area[J]. Modern defence technology,2016,44(2):190-196.