基于连续Hopfield网络的反导火力分配优化

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。

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

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.