To solve the problem of truck's overload running and related transportation management,a vehi-cle on-board weighing system based on BP neural network is proposed. By measuring the tiny deformation on truck axles caused by load, second-order lowpass filtering and digital filtering algorithms are designed to extract effective load data. Then load model is built by using BP neural network, and according to the sample data obtained in load-ing test on a light van with weighing system installed, a process of learning, testing and prediction of neural network is completed by adopting Levenberg-Marquardt learning algorithm in Matlab neural network tool box. The results show that the error of predicted load is within 5%, meeting engineering requirements and indicating the feasibility of proposed scheme.%为解决汽车超载运行和相应的运输业管理问题,本文中提出了一套基于BP神经网络的车载称重系统.通过检测车桥随载荷量变化而产生的微小变形,设计了2阶低通滤波和数字滤波算法,以提取有效载荷信息,利用BP神经网络建立载荷模型,并根据在某轻型厢式货车上进行的试装和加载试验得到的样本数据,在Matlab神经网络工具箱中,采用Levenberg-Marquardt学习算法完成了神经网络的学习、检验和预测.结果表明:预测载荷误差在5%以内,满足工程要求,方案可行.
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