Support vector machine model for spacecraft single wall ballistic limit prediction

An approach for spacecraft single wall ballistic limit prediction is proposed based on nonlinear nonseparable support vector machine (SVM). The test data are used to train the SVM, and the separation plane of both penetration and non-penetration points is built. The trained SVM can be used for new case predictions. The training problem of SVM is a quadratic programming problem. The constraint of the optimization is the correctness of the test data classification and the aim of the optimization is maximizing the confidence of the classification. The Lagrangian dual theory is introduced to solve the training problem. With adding the upper boundary to the Lagrangian multipliers, the nonseparable data set is handled. Quadratic kernel function is introduced to extend the SVM approach to nonlinear problem. The test data are effectively classified with the quadratic kernel function SVM. The hypervelocity impact test data are used for the SVM prediction model verification. The results show that the SVM approach is feasible, and the accuracy is higher than NASA JSC single wall ballistic limit equation. By separating the variable of projectile diameter from the classifier equation, the SVM ballistic limit equation is built.