摘要
利用滞留车辆与绿信比的关系建立了以最短等待时间为优化目标的数学模型,并通过免疫遗传算法得到点上最优解,将混沌交通流序列再引入点问题模型中,运用指数平滑预测模型为BP神经网络模型提供学习所需数据,从而得到混沌交通流序列的实时配时方案。
The relation of waiting vehicles and split is utilized to establish the mathematical model with the shortest waiting time as optimization target, and by the immune genetic algorithm to get the point with the optimal solution. Then introducing chaotic sequence of traffic flow into the point problem model, using exponential smoothing forecasting model to provide the necessary data of learning for BP neural network model, the real-time timing scheme for chaotic sequence of traffic flow is obtained.
出处
《计算机时代》
2010年第6期22-25,共4页
Computer Era
关键词
实时控制
免疫遗传算法
指数平滑预测模型
BP神经网络模型
real-time control
immune genetic algorithm
exponential smoothing forecasting model
BP neural network model