College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing 400044, China.
State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2022 Jun 21;22(13):4676. doi: 10.3390/s22134676.
We propose an improved DNN modeling method based on two optimization algorithms, namely the linear decreasing weight particle swarm optimization (LDWPSO) algorithm and invasive weed optimization (IWO) algorithm, for predicting vehicle's longitudinal-lateral responses. The proposed improved method can restrain the solutions of weight matrices and bias matrices from falling into a local optimum while training the DNN model. First, dynamic simulations for a vehicle are performed based on an efficient semirecursive multibody model for real-time data acquisition. Next, the vehicle data are processed and used to train and test the improved DNN model. The vehicle responses, which are obtained from the LDWPSO-DNN and IWO-DNN models, are compared with the DNN and multibody results. The comparative results show that the LDWPSO-DNN and IWO-DNN models predict accurate longitudinal-lateral responses in real-time without falling into a local optimum. The improved DNN model based on optimization algorithms can be employed for real-time simulation and preview control in intelligent vehicles.
我们提出了一种基于两种优化算法（即线性递减权重粒子群优化（LDWPSO）算法和入侵杂草优化（IWO）算法）的改进 DNN 建模方法，用于预测车辆的纵向-横向响应。所提出的改进方法可以在训练 DNN 模型时防止权重矩阵和偏差矩阵的解陷入局部最优。首先，基于有效的半递归多体模型进行车辆的动态仿真，以进行实时数据采集。接下来，处理车辆数据并用于训练和测试改进的 DNN 模型。将从 LDWPSO-DNN 和 IWO-DNN 模型获得的车辆响应与 DNN 和多体结果进行比较。比较结果表明，LDWPSO-DNN 和 IWO-DNN 模型可以实时预测准确的纵向-横向响应，而不会陷入局部最优。基于优化算法的改进 DNN 模型可用于智能车辆的实时仿真和预瞄控制。
