Title :
The shift system of automated mechanical transmission based on neural network control
Author :
Lu Zeng ; Jun Liu ; Yong Qin ; Wang ZiYang
Author_Institution :
State Key Lab. of Rail Traffic Control & Safety, Beijing Jiaotong Univ., Beijing, China
Abstract :
Automated mechanical transmission technology is very suitable for the development of China´s automobile industry realities. The paper discusses optimizing shift regularity of AMT and training simulation of data is completed by neural network. It indicated that distinguish measure of RBF neural network can solve the problem of shift recognition, and offer the shift control strategy of neural network. The research of the paper provides theory basis for improving design and practical application.
Keywords :
automobiles; neurocontrollers; power transmission (mechanical); radial basis function networks; China; RBF neural network; automated mechanical transmission technology; automobile industry; neural network control; radial basis function neural network; shift control strategy; shift recognition; shift regularity optimization; shift system; Acceleration; Apertures; Joints; Neural networks; Synchronous motors; Traction motors; Vehicles; automated mechanical transmission; clutch engagement; neural network; optimizing shift;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2014 11th World Congress on
DOI :
10.1109/WCICA.2014.7053397
