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首页> 外文期刊>JSME International Journal. Series C, Mechanical Systems, Machine Elements and Manufacturing >Design Optimization for Suspension System of High Speed Train Using Neural Network
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Design Optimization for Suspension System of High Speed Train Using Neural Network

机译：基于神经网络的高速列车悬架系统设计优化

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摘要

Design optimization has been performed for the suspension system of high speed train. Neural network and design of experiment (DOE) have been employed to build a meta-model for the system with 29 design variables and 46 responses. A combination of fractional factorial design and D-optimality design was used as an approach to DOE in order to reduce the number of experiments to a more practical level. As a result, only 66 experiments were enough. The 46 responses were divided into four performance index groups such as ride comfort, derailment quotient, unloading ratio and stability index. Four meta-models for each index group were constructed by use of neural network. For the learned meta-models, multi-criteria optimization was achieved by differential evolution. The results show that the proposed methodology yields a highly improved design in the ride comfort, unloading ratio and stability index.
机译：已经对高速列车的悬架系统进行了设计优化。神经网络和实验设计（DOE）已被用来为系统建立具有29个设计变量和46个响应的元模型。分数阶乘设计和D优化设计的组合被用作DOE的一种方法，以将实验次数减少到更实际的水平。结果，仅66个实验就足够了。 46个响应分为四个性能指标组，例如乘坐舒适性，出轨商，卸载率和稳定性指标。利用神经网络为每个指标组构建了四个元模型。对于学习的元模型，通过差异演化实现了多准则优化。结果表明，所提出的方法在乘坐舒适性，卸载率和稳定性指标方面产生了高度改进的设计。

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