School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel). 2022 Mar 9;22(6):2109. doi: 10.3390/s22062109.
Weigh-in-motion (WIM) systems are used to measure the weight of moving vehicles. Aiming at the problem of low accuracy of the WIM system, this paper proposes a WIM model based on the beetle swarm optimization (BSO) algorithm and the error back propagation (BP) neural network. Firstly, the structure and principle of the WIM system used in this paper are analyzed. Secondly, the WIM signal is denoised and reconstructed by wavelet transform. Then, a BP neural network model optimized by BSO algorithm is established to process the WIM signal. Finally, the predictive ability of BP neural network models optimized by different algorithms are compared and conclusions are drawn. The experimental results show that the BSO-BP WIM model has fast convergence speed, high accuracy, the relative error of the maximum gross weight is 1.41%, and the relative error of the maximum axle weight is 6.69%.
动态称重（WIM）系统用于测量移动车辆的重量。针对 WIM 系统精度低的问题，本文提出了一种基于甲壳虫群优化（BSO）算法和误差反向传播（BP）神经网络的 WIM 模型。首先，分析了本文所用 WIM 系统的结构和原理。其次，通过小波变换对 WIM 信号进行去噪和重构。然后，建立了基于 BSO 算法优化的 BP 神经网络模型来处理 WIM 信号。最后，对比了不同算法优化后的 BP 神经网络模型的预测能力，并得出结论。实验结果表明，BSO-BP WIM 模型具有较快的收敛速度、较高的精度，最大总重的相对误差为 1.41%，最大轴重的相对误差为 6.69%。
