Journal of System Simulation >
Combat Effectiveness Evaluation of Air Defense Missile Weapon System Based on RBF Neural Network
Received date: 2023-12-25
Revised date: 2024-01-10
Online published: 2025-02-10
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.
Zhang Peng , Feng Ke , Gong Jiancheng , Yang Xiaoqiang , Shen Jinxing . Combat Effectiveness Evaluation of Air Defense Missile Weapon System Based on RBF Neural Network[J]. Journal of System Simulation, 2025 , 37(2) : 529 -540 . DOI: 10.16182/j.issn1004731x.joss.23-1571
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