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 eval-uation 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 effec-tiveness 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 pro-posed 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 ex-pected output and the actual output and it has high fitting accuracy,which means this method has high re-liability and feasibility.
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