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

Abstract

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.

Description

technical field [0001] The invention mainly relates to the field of safety optimization design in vehicle design and manufacture, in particular to a parallel support vector machine approximation model optimization method based on automobile crashworthiness. Background technique [0002] Crashworthiness is the main consideration in the optimal design of automobile safety, and what affects automobile safety is often the parts provided to the automobile for deformation and energy absorption during the design process, such as front longitudinal beams, crash boxes, etc. These key absorbing The characteristics and deformation modes of energy-absorbing components determine the force or acceleration of the car during the collision process, which plays a vital role in occupant protection, so thin-walled energy-absorbing components have always been a focus of vehicle optimization design. [0003] The early optimization of crashworthiness problems was based on gradient classical optimi...

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Problems solved by technology

[0006] The main defect of the above-mentioned SVM method lies in its optimization efficiency. Since a large number of samples are required to build a robust approximate model, for the complex vehicle crashworthiness problem, the SVM method requires a large number of calculation samples, which is difficult to apply to engineering practice.

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