基于遗传算法径向基神经网络的交通流预测.doc

文档介绍：

基于遗传算法径向基神经网络的交通流预
测
楼旭伟 1,楼辉波 1,朱剑锋 2
(1. 奉化市交通投资公司,浙江奉化 315500;
2. 宁波大学建筑工程与环境学院, 浙江宁波 315211)
摘要:为提高径向基(RBF)神经网络预测模型对交通流预测的准确性,提出了一种基于遗传算法优化径向基
神经网络的交通流预测方法。利用遗传算法优化径向基神经网络的权值和阈值,然后训练RBF神经网络预
测模型以求得最优解,并将该预测方法与RBF神经网络和BP神经网络的预测结果进行对比。仿真结果表明,
该方法对交通流具有较好的非线性拟合能力,预测精度高于径向基神经网络和BP神经网络。
关键词: 交通流;遗传算法;RBF 神经网络;BP 神经网络
Prediction of traffic flow of optimized radial basis function
work based on ic algorithm
LOU Xuwei1, LOU Huibo1, ZHU Jianfeng2
(1. munications pany, Fenghua, Zhejiang 315500, China;
2. Faculty
of Architectural Civil Engineering and Environment, Ningbo University, Ningbo, Zhejiang
315211, China)
Abstract: In order to improve the prediction accuracy of radial basis function (RBF) work
model for predicting traffic flow, a prediction method for traffic flow of optimized RBF work
based on ic algorithm (GA) is presented. The GA is used to optimize the weights and thresholds
of RBF work, and the RBF work is trained to search for the optimal solution. The
efficiency of the proposed prediction method is tested parison with the results predicted by RBF
and BP work. The simulation results show that the proposed method has better nonlinear
fitting ability for the prediction of traffic flow. Moreover, it has better fitting ability and higher
accuracy than RBF and BP work.
Key words: traffic flow; ic algorithm; RBF work; BP work
实时准确预测交通流是隧道前馈式通风控