摘要
针对高速公路限速控制是一个非线性时变系统、难以用数学模型准确建模这一特点,提出了RBF神经网络控制方法。阐述了RBF神经网络的结构和训练方法,根据高速公路主线上车辆数目以及路面状况、气象条件等信息,建立交通流速度控制RBF神经网络模型,并进行了仿真研究。该网络学习速度快、自适应强,泛化能力好,对交通流限速控制的在线建模具有重要意义。
The control for speed limitation on freeway is a nonlinear and time variable system,it is difficult to model with a mathematical model.A control method based on RBF Neural Network is put forward.The network structure and train algorithm are formulated.The RBF network model for speed limitation of freeway traffic is built according to such information as the number of vehicles on freeway,the performance of road surface,and the weather conditions.Simulation research is also carried out by taking full advantage of a computer.The fast learning ability,strong adaptability,and good generality are of great importance to realize on-line modeling for speed limitation of traffic flow.
出处
《计算机工程与应用》
CSCD
北大核心
2005年第6期194-195,201,共3页
Computer Engineering and Applications
基金
广东省自然科学基金项目(编号:010486)资助
