基于混沌双扰动的CDDP SO-BP目标威胁估计方法?

信息化条件下的战场环境对目标威胁估计技术提出了越来越高的要求。提出一种基于混沌双扰动的CD?DPSO?BP目标威胁估计方法，该方法针对粒子群算法在进化过程中易出现早熟和寻优结果不稳定的缺陷，基于Tent映射提出混沌双扰动的思想，并加入粒子群算法的进化过程，实现对粒子群算法的改进；之后，利用该新算法训练BP神经网络的初始权值和阈值，建立目标威胁估计模型和算法；最后，将该方法应用于实例中进行仿真，结果表明该目标威胁估计新算法具有较高的准确度。

CDDP SO-BP Target Threat Estimation Method Based on Chaos Double Disturbance

Target threat assessment technology has been proposed higher and higher request by battlefield environment under information condition. A kind of CDDPSO?BP target threat estimation method based on chaotic double disturbance is put for?ward. Focusing on the defect of particle swarm algorithm easy to falling into precocious and unstable in searching the best val?ue, a chaotic double disturbance thinking based on Tent map is proposed and applied to the evolutionary process of particle swarm algorithm to improve the PSO algorithm. Then, the initial weights and thresholds of the BP neural network have been trained with the new algorithm, so that the target threat assessment model and algorithm is established. Finally, the simula?tion result proves that the proposed target threat estimation algorithm has higher accuracy.