Title :
The Research of urban road tunnel longitudinal ventilation control based on RBF neural network
Author :
Du, Pengying ; Luo, Xiaoping
Author_Institution :
Key Lab. of Intell. Syst., Zhejiang Univ. City Coll., Hangzhou, China
Abstract :
The urban road tunnel longitudinal ventilation system has strong time-varying, non1inear and large delay characteristics and it is difficult to get the precise mathematical model, Conventional linear control theories are inefficient. RBF (Radial Basis Function) neural network is adopted in urban road tunnel longitudinal ventilation control system considering traffic flow, CO (Carbon monoxide), VI (Visibility) values and other factors. In normal traffic flow, density traffic flow, sparse traffic flow different situations for tunnel ventilation, test simulation results show that this control method is better and it is more efficient than the conventional method of nearly 15%.
Keywords :
geotechnical engineering; neurocontrollers; nonlinear control systems; radial basis function networks; road traffic; structural engineering; tunnels; density traffic flow; linear control theories; radial basis function neural network; sparse traffic flow; urban road tunnel longitudinal ventilation control system; Artificial neural networks; Fans; Intelligent control; Radial basis function networks; Roads; RBF; energy conservation; traffic flow; urban road tunnel; ventilation control;
Conference_Titel :
Geoscience and Remote Sensing (IITA-GRS), 2010 Second IITA International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-8514-7
DOI :
10.1109/IITA-GRS.2010.5603278