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

Author

Qi, Zhang ; Xiufen, Xie ; Guofu, Liu ; Bo, Liu

Author_Institution

Nat. Univ. of Defense Technol., Changsha

fYear

2007

fDate

Aug. 16 2007-July 18 2007

Firstpage

26755

Lastpage

27851

Abstract

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 it´s effective and has high quality of anti-noisy.

Keywords

automotive electronics; backpropagation; neural nets; traffic engineering computing; velocity control; wheels; RPROP; modern car control systems; resilient back propagation neural network; sensor angular errors; sensor reliability; wheel speed sensor errors; Error correction; Instruments; Intelligent sensors; Magnetic sensors; Mechanical sensors; Neural networks; Teeth; Tires; Velocity measurement; Wheels; resilient back propagation (RPROP); sensor error; wheel speed;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronic Measurement and Instruments, 2007. ICEMI '07. 8th International Conference on

Conference_Location

Xi´an

Print_ISBN

978-1-4244-1136-8

Electronic_ISBN

978-1-4244-1136-8

Type

conf

DOI

10.1109/ICEMI.2007.4351247

Filename

4351247