Title :
Vehicle simulation and test system based on RBFNN and its improved algorithm
Author :
Jiang, Yicheng ; Sun, Sibo
Author_Institution :
Dept. of Electron. Eng., Harbin Inst. of Technol., Harbin, China
Abstract :
According to the characteristics of the vehicle simulation and test system, we set up the vehicle acceleration and engine speed model, and RBF neural network is applied in this model. Since ordinary RBF network has poor adaptability, NRBF and Classified-RBF is proposed in this paper. The simulation results show that both methods can fit the model well, and the output error actually decreases to about half. The adaptability of the RBF network is improved. These can be used in the data fusion of the vehicle simulation and test system.
Keywords :
automobile industry; engines; radial basis function networks; simulation; testing; vehicles; RBF neural network; RBFNN; classified-RBF; engine speed model; test system; vehicle acceleration; vehicle simulation; Adaptation models; Classification algorithms; Engines; Training; Vehicles; NRBF; RBF neural network; simulation; vehicle test system;
Conference_Titel :
Computer Science and Information Processing (CSIP), 2012 International Conference on
Conference_Location :
Xi´an, Shaanxi
Print_ISBN :
978-1-4673-1410-7
DOI :
10.1109/CSIP.2012.6308972
