用深度学习预测多轴车辆悬架动态性能，提升建模精度。
Deep Learning-Based Prediction of Suspension Dynamics Performance in Multi-Axle Vehicles
- 构建多任务深度信念网络-深度神经网络模型捕捉参数与性能关系。
- 相比传统DNN，预测准确率显著提升，验证了模型有效性。
- 提出悬架动态性能指数SDPI，综合评估多参数影响，适合车辆设计优化。
本文提出一种基于深度学习的框架，用于预测多轴车辆悬架系统的动态性能，强调机器学习与传统车辆动力学建模的融合。开发了多任务深度信念网络-深度神经网络（MTL-DBN-DNN）模型，以捕捉关键车辆参数与悬架性能指标之间的关系。模型基于数值仿真生成的数据训练，预测精度优于传统DNN模型。通过全面的敏感性分析，评估了各类车辆及悬架参数对动态悬架性能的影响。此外，提出了悬架动态性能指数（SDPI），作为综合衡量整体悬架性能的指标，考虑多个参数的协同效应。研究结果表明，多任务学习在复杂车辆系统预测模型中具有显著优势。
原文摘要 · Abstract (English)
This paper presents a deep learning-based framework for predicting the dynamic performance of suspension systems in multi-axle vehicles, emphasizing the integration of machine learning with traditional vehicle dynamics modeling. A Multi-Task Deep Belief Network Deep Neural Network (MTL-DBN-DNN) was developed to capture the relationships between key vehicle parameters and suspension performance metrics. The model was trained on data generated from numerical simulations and demonstrated superior prediction accuracy compared to conventional DNN models. A comprehensive sensitivity analysis was conducted to assess the impact of various vehicle and suspension parameters on dynamic suspension performance. Additionally, the Suspension Dynamic Performance Index (SDPI) was introduced as a holistic measure to quantify overall suspension performance, accounting for the combined effects of multiple parameters. The findings highlight the effectiveness of multitask learning in improving predictive models for complex vehicle systems.
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