基于自组织竞争神经网络的智能交通信号灯控制

为了解决拥挤的城市交通问题，针对交通灯监控系统中可变的交通状况，提出一种基于自组织神经网络算法的监控系统。应用优化模型参实现交通灯的控制，使道路通畅。对监控系统进行分析，合理选择优化的模型参数，根据动态的车流量，通过模式分类有效分配当前车道的通行时间，并全面考虑同时通行的各种车道组合。该监控系统提高了车辆通行效率，使道路更为通畅。与传统的固定配时系统相比，它更适于当前纷繁复杂的交通状况。

The Intelligent Traffic Signal Lamp Control based on Self-Organizing Competitive ANN

In order to solve the crowded transportation problem of cities,in view of the variable traffic condition in the traffic lights monitoring system,proposed to use the self-organizing competitive ANN to control to realize the control of the traffic lights.It assigned the pass time of each traffic lane through optimize the model parameters according to the dynamic volume of vehicle.In the paper it has considered all kinds of traffic lanes combination pass through model classifying in the same time.The monitoring system enhanced the efficiency of vehicles traffic,caused the path to be more unobstructed.Compared with the traditional fixed timing systems,it is more suitable to the complexity of current traffic conditions.