Influencing Factor Analysis of Interception Probability and Classification-Regression Neural Network Based Estimation

The interception probability of a single missile is the basis for combat plan design and weapon performance evaluation, while its influencing factors are complex mutually coupled. Existing calculation methods have very limited analysis influence mechanism factors, none them has analyzed guidance law. This paper considers both interceptor target more comprehensively. Interceptor parameters include speed, law, error, fuze fragment killing ability, includes maneuverability, vulnerability. In this paper, an model established, Monte Carlo simulation carried out, each factor based on results. Finally, proposes classification-regression neural network to quickly estimate value factors. proposed method reduces interference invalid data valid data, so prediction accuracy significantly better than that pure regression networks.