Parallel support vector machine approximate model optimization method based on automobile crashworthiness

The invention relates to a parallel support vector machine approximate model optimization method based on automobile crashworthiness. The parallel support vector machine approximate model optimization method comprises the steps of (1) establishing a network; (2) generating initial samples, and automatically transferring the initial samples to a grid node; (3) distributing the initial samples to calculating nodes; (4) generating sample points at random; (5) establishing an approximate model based on SVM (Support Vector Machine); (6) obtaining an error standard of an SVM approximate model, and judging whether the error standard reaches the convergence level or not; if so, constructing an entire SVM model by adopting all the generated nodes; (7) judging whether the entire SVM model converges or not; if so, finishing the process, otherwise, skipping to the step (8); (8) finding out the maximum error region; and (9) calculating the sum of the information of the error region, generating samples at random in the region after the summation, uniformly distributing the samples to the calculation nodes, and skipping to the step (4) till the process is finished. According to the parallel support vector machine approximate model optimization method, the mode that the SVM (support vector machine) is subjected to parallel processing is adopted, so that the modeling speed is greatly increased and the optimization efficiency and the precision are improved.