Attenuating the Wheel Speed Sensor Errors Based on Resilient Back Propagation Neural Network

摘要：

Wheel speed is a very important control signal in modern car control systems. The quality of the processed wheel speed determines the performance of these systems. However, the quality of the signal is not so good due to manufacturing tolerances or wear and tear of the sensor. In this paper a method to compensate for the mechanical inaccuracy of the sensor is presented. We train Resilient Back Propagation (RPROP) neural network by utilizing large amounts of sensor angular errors to correct the wheel speed. The results by simulation show that its effective and has high quality of anti-noisy.

关键词： wheel speed sensor error resilient back propagation (RPROP)

作者: Zhang Qi Xie Xiufen Liu Guofu Liu Bo

作者单位: Department of Instrument Science and Technology,National University of Defense Technology Changsha,410073 China

会议类型: 国际会议

会议名称: 第八届国际电子测量与仪器学术会议(Proceedings of 2007 8th International Conference on Electronic Measurement & Instruments)

会议地点: 西安

会议语种:英文

在线出版日期: 2007-08-16（万方平台首次上网日期，不代表论文的发表时间）