基于神经网络的防空武器目标智能分配方法

Neural Network Based Air Defense Weapon Target Intelligent Assignment Method

为满足现代防空作战高动态环境对武器目标实时智能分配的迫切需求,提出了一种基于神经网络的防空武器目标智能分配方法。首先综合考虑武器数量及拦截能力等要素,以毁伤效能最大为优化目标,建立武器目标分配问题优化模型;在此基础上,构建基于神经网络的武器目标智能分配框架,基于分配方案数据集训练武器分配决策神经网络,并通过神经网络预测实现当前武器资源对威胁目标的准确高效分配。仿真结果表明,相比于传统基于离散差分进化的规划求解方法,基于神经网络的防空武器目标智能分配方法能够获取毁伤效能更优的武器目标分配方案,且计算成本降低了99.9%以上,从而验证了所提方法的有效性和实用性。

To address the challenge of the real-time weapon target intelligent assignment in modern high dynamic air defense operation,a neural network based air defense weapon target intelligent assignment method is proposed in this paper.An optimization model for weapon target assignment is first established considering the number of weapons and interception capability in order to optimize the damage efficiency.A neural network based weapon target intelligent assignment framework is then developed,where a neural network is constructed based on the existed training sample set and employed to assign the weapons to the targets effectively and efficiently.The comparison results illustrate that the neural network based air defense weapon target assignment method can obtain better weapon target assignment solution compared with the conventional discrete differential evolution based assignment method.Moreover,the computational cost of the proposed method can be reduced by 99.9%,which demonstrates the effectiveness and practicability of the research work.