基于RBF神经网络的防空导弹武器系统作战效能评估  

Combat Effectiveness Evaluation of Air Defense Missile Weapon System Based on RBF Neural Network

作　　者：张鹏 冯柯 宫建成 杨小强 申金星 Zhang Peng;Feng Ke;Gong Jiancheng;Yang Xiaoqiang;Shen Jinxing(College of Field Operations Engineering,Army Engineering University of PLA,Nanjing 210000,China)

机构地区：[1]陆军工程大学野战工程学院,江苏南京210000

出　　处：《系统仿真学报》2025年第2期529-540,共12页Journal of System Simulation

摘　　要：针对防空导弹武器系统作战效能指标维度高、复杂性强以及评估方法主观等问题,提出基于RBF神经网络的作战效能评估方法。通过分析OODA环作战理论,构建了防空导弹武器系统作战效能指标体系。通过MATLAB实现RBF神经网络模型仿真,并应用BP、PCA-BP、Elman神经网络等多种方法进行对比仿真验证。仿真结果表明:RBF神经网络模型预测评估结果与真实值更为接近,充分证明了该模型在防空导弹武器系统作战效能评估中的有效性,为指挥员作战决策提供有力支持。A combat effectiveness evaluation method based on RBF neural network is proposed to address the problems of high dimensionality,high complexity,and subjective evaluation methods in current air defense missile weapon systems.A combat effectiveness index system for air defense missile weapon systems has been constructed by analyzing the OODA environmental combat theory.The RBF neural network model simulation is implemented using MATLAB,and several methods such as BP,PCA-BP,and Elman neural network are compared and verified through simulation.The simulation results show that the predicted evaluation results of the RBF neural network model are closer to the actual values,fully proving the effectiveness of the model in evaluating the combat effectiveness of air defense missile weapon systems and providing strong support to commanders in making operational decisions.

关 键 词：RBF神经网络 防空导弹武器系统 效能评估 作战仿真 OODA环 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] E911[自动化与计算机技术—计算机科学与技术]