Title :
Application of RBF neural network to freeway traffic flow modeling
Author_Institution :
Dept. of Electr. & Autom., Shanghai Maritime Univ., China
Abstract :
Freeway traffic flow is a nonlinear and time-variant system, the application of radial basis function (RBF) neural network to freeway macroscopic traffic flow dynamic modeling is presented. A learning algorithm of subtractive clustering method is used to obtain the parameters of radial basis function in this paper, so that RBF neural network has an optimized structure. The simulation results show the algorithm is effective for freeway traffic flow modeling.
Keywords :
learning (artificial intelligence); nonlinear control systems; radial basis function networks; road traffic; time-varying systems; traffic control; RBF neural network; freeway traffic flow modeling; learning algorithm; nonlinear system; time-variant system; Automation; Clustering algorithms; Clustering methods; Electronic mail; Neural networks; Nonlinear dynamical systems; Optimization methods; Telecommunication traffic; Traffic control;
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
DOI :
10.1109/WCICA.2004.1343714
