基于IPSO-SVR的反导装备体系效能评估方法研究

Research on Effectiveness Evaluation Method in Anti-Missile Equipment System Based on IPSO-SVR

鉴于反导装备体系运行机理复杂、结构不清难以选择合适的效能评估模型等问题,采用“数据驱动+深度学习”的方法对反导装备体系效能评估展开研究。结合反导装备体系作战过程,从探测跟踪、指挥控制、火力拦截和综合保障4个方面构建了反导装备体系效能评估指标;针对PSO算法容易陷入局部极值、早熟收敛等问题,提出改进型粒子群优化算法,对SVR参数进行优化,建立了IPSO-SVR效能评估模型;在大量反导装备体系实验数据抽取、处理、分析的基础上,对IPSO-SVR模型进行训练和学习,以此获得对反导装备体系效能的非线性拟合。实验结果表明:所提效能评估方法期望输出和实际输出之间误差非常小,拟合精准度高,具有较高的可靠性和可行性。

In view of the complex operation mechanism of anti-missile equipment system, the unclear structure which makes it difficult to select a suitable efficiency evaluation model, so the effectiveness evaluation of anti-missile equipment system is studied by the method of "data-driven + deep learning". Based on the operational process of the anti-missile equipment system, the evaluation index of the effectiveness of the anti-missile system is constructed from four aspects: detection and tracking, command and control, firepower interception and integrated support. To solve the problems of PSO algorithm, such as local extremum and premature convergence, an improved particle swarm optimization algorithm is proposed to optimize the parameters of SVR, and an IPSO-SVR efficiency evaluation model is established. On the basis of extracting, processing and analyzing a large number of experimental data, the IPSO-SVR model is trained and studied to obtain nonlinear fitting of the effectiveness of the anti-missile equipment system. The experimental results show that the proposed method has a very small error between the expected output and the actual output and it has high fitting accuracy, which means this method has high reliability and feasibility.