计算机工程与应用Issue(9)：106-109,4.DOI:10.3778/j.issn.1002-8331.1212-0188
CS 算法优化 BP 神经网络的短时交通流量预测
Short time traffic flow prediction model based on neural network and cuckoo search algorithm
高述涛1
作者信息
- 1. 湖南外贸职业学院 服务外包学院，长沙 410014
- 折叠
摘要
Abstract
In order to improve the prediction accuracy of short time traffic flow,this paper proposes a network traffic prediction model based on Cuckoo Search algorithm and BP Neural Network(CS-BPNN).The time series of short time traffic flow is recon-structed to form a multidimensional time series based on chaotic theory,and then the time series are input into BP neural net-work to learn which parameters of BP neural network are optimized by cuckoo search algorithm to find the optimal parameters and establish the short time traffic flow prediction model.The performance of CS-BPNN is tested by the simulation experiments. The simulation results show that the proposed model improves the prediction accuracy of short time traffic flow and can more describe network traffic complex trend compared with reference models.关键词
短时交通流量/相空间重构/布谷鸟搜索算法/高斯扰动/反向传播(BP)神经网络Key words
short time traffic flow/phase space reconstruction/cuckoo search algorithm/Gaussian disturbance/Back Propaga-tion(BP)neural network分类
信息技术与安全科学引用本文复制引用
高述涛..CS 算法优化 BP 神经网络的短时交通流量预测[J].计算机工程与应用,2013,(9):106-109,4.
