Research on Joint Firepower Strike Effectiveness Assessment Based on Deep Learning
QI Zhimin
MA Xianming
CHEN Min
HU Rui
Abstract：To solve the problem of"relying on experience and weak dynamic adaptability"in the evaluation of joint firepower strike effectiveness in military chess deduction scenarios,this study relies on the advantages of deep learning in processing complex data and modeling.Firstly,combined with the actual background of"multi firepower platform collaboration and dynamic changes in strike situation"in joint firepower strikes,the characteristics and correlation logic of the evaluation data are analyzed.Then this study designs a basic model that is suitable for practical use,using the strike plan as input and rule calculation evaluation results as labels to construct a sample set and complete the training of the intelligent evaluation model.There are two verification methods,one is to compare the consistency between the output results of the intelligent model and the commander's experiential cognition.The sec-ond is to use the RMSE index to analyze the performance deviation between the real results of the test set,the output values of the in-telligent model,and the expert adjusted evaluation results.The results indicate that the deviations of the three types of results are within an acceptable range,which confirms the feasibility and practical value of the design ideas in the evaluation of joint firepower strike effectiveness,and can provide technical support for the optimization of joint firepower strike schemes and intelligent deci-sion-making.
Keywords：deep learningfirepower strike effectivenesswargameperformance evaluation
Publication Date：2025-12-20
Online Publishing Date：2026-03-23(First online date of this platform, not the publication date of the document)
Pages：5( 155-159 )