【摘要】 提出了一种基于BP神经网络的轮胎气压监测系统轮胎换位自学习匹配方法。该方法基于间接式轮胎压力监测系统和轮胎受力特性对换位后的轮速信号特征进行分析,运用BP神经网络识别轮胎换位方式。通过采集轮胎换位后各车轮轮速数据对BP神经网络进行训练,从而实现神经网络对轮胎换位的准确识别,使得TPMS在无人工干预下可自行识别轮胎换位状态。道路试验结果表明,完成训练后的网络可实现对未换位、交叉换位、前后换位和循环换位的有效识别,准确率达97.52%。更多还原

【Abstract】 In this paper, a self-learning tire matching method based on BP neural network for TPMS was proposed.This method analyzed wheel speed signal feature based on tire force characteristic and indirect TPMS, and used BP neural network to identify the tire rotation. By acquiring wheels speed data of tire rotation, BP neutral network was trained, that enabled accurate recognition of tire rotation, helping the TPMS to automatically recognize tire rotation state without manual intervention. Road test results show that the trained BP neural network can effectively recognize the modes between no rotation, cross rotation, front-rear rotation and cycle rotation, with correct recognition rate up to 97.52%.更多还原