摘要
高速公路限速控制是一个非线性时变系统,难于用数学模型准确建模,提出一种模糊神经网络实现限速控制。本文阐述了网络的结构和学习算法,根据高速公路车辆群状态、路面性能、气象条件等,建立交通流速度限制模糊神经网络模型,并进行了仿真研究。仿真结果表明网络训练速度快、精度高,适合交通流限速控制的在线建模。该方法切实可行,可使交通流更加均匀、稳定,从而提高主线运行的安全和效率。
The control for speed limit on expressway is a nonlinear and time variable system, it is difficult to simulate with a mathematical model.A neuro-fuzzy network is proposed to solve the problem. The network structure and learning algorithms are formulated. The network model is built based on such information as the number of vehicles on expressway, the performance of the road surface, and the weather conditions.Simulation study is carried out by taking full advantage of a computer.Simulation results show that such a network has fast learning ability and high accuracy.It is suitable to realize on-line modeling for speed limit of expressway traffic. The approach is practical and effective. It can make the traffic flow more uniform and steady, so that the safety and efficiency on expressway are improved.
出处
《公路交通科技》
CAS
CSCD
北大核心
2005年第11期123-125,129,共4页
Journal of Highway and Transportation Research and Development
基金
广东省自然科学基金资助项目(010486)
