基于连续Hopfield网络的反导火力分配优化  

A Study of the Optimal Anti-missile Firepower Distribution Based on Continuous Hopfield Neural Networks

作　　者：方逸洪[1] 李为民[1] 周晓光[1] 钟秋 

机构地区：[1]空军工程大学导弹学院,陕西三原713800 [2]空军装备部,北京100843

出　　处：《空军工程大学学报（自然科学版）》2011年第6期32-38,共7页Journal of Air Force Engineering University(Natural Science Edition)

基　　金：国家"863"计划资助项目(2009AA701XXX)

摘　　要：为缩短防空火力分配模型解算时间,提高防空火力分配的鲁棒性,应用Hopfield神经网络对反导火力分配问题进行了研究。以反导火力分配为研究对象,建立了反导火力分配模型,以提高反导战场管理的智能化。提出了基于连续Hopfield神经网络的反导火力分配优化算法,并对该算法进行了收敛性和稳定性分析;最后应用实例验证了模型的有效性。Anti-missile firepower distribution is one of the key tasks of BM,the firepower distribution model and the efficiency of solving it affect the result of the anti-missile defense warfare directly.The research on anti-missile firepower distribution is done,and the model of anti-missile firepower distribution is built.This paper presents a continuous Hopfield neural network-based algorithm for the optimization of the anti-missile firepower distribution and analyzes the convergence and stability.Finally,three representative examples are solved by the method presented in this paper,and the numerical results are present.

关 键 词：神经网络 反导 火力分配 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]