基于神经网络的车辆抗冲击防护组件优化

随着军用车辆防护要求的不断提高，抗冲击防护组件设计面临越来越多的挑战。为了提供一种高效科学的研究方法，本文中采用V型结构，并应用径向基函数神经网络近似模型和多目标遗传算法对某型车防护组件进行优化设计。以防护组件变形量与总体质量最小为设计目标，利用灵敏度分析筛选出对防护组件防护性能影响较大的设计因子。以径向基函数神经网络构建实验设计样本的近似模型，然后用多目标遗传算法进行数值优化获得防护组件最优方案。最后通过仿真与实验验证，证明优化方案满足设计要求。研究结果可为今后防护组件开发提供设计思路。

Research on optimization of vehicle anti-shock protection components based on neural network

With the increasing requirements for the protection of military vehicles, the design of impact protection components is facing more and more challenges. In order to provide an efficient and scientific research method, this paper adopts a V-shaped structure, and uses radial basis function neural network approximation model and multi-objective genetic algorithm to optimize the design of a certain type of vehicle protection components. Taking the deformation amount of the protection component and the total mass as the design goal, the sensitivity analysis is used to select the design factor that has a great influence on the protection performance of the protection component. The approximate model of the experimental design sample is constructed by radial basis function neural network, and then multi-objective genetic algorithm is used to numerically optimize the optimal component of the protection component. Finally, through simulation and experimental verification, it is proved that the optimization scheme meets the design requirements. Provide a design idea for the future development of protective components.