计算机应用与软件2017，Vol.34Issue(3)：238-242,5.DOI:10.3969/j.issn.1000-386x.2017.03.043
布谷鸟算法优化小波神经网络的短时交通流预测
PREDICTION FOR SHORT-TERM TRAFFIC FLOW BASED ON WAVELET NEURAL NETWORK OPTIMISED BY CUCKOO SEARCH ALGORITHM
黄晓慧 1张翠芳1
作者信息
- 1. 西南交通大学信息科学与技术学院 四川 成都 611756
- 折叠
摘要
Abstract
Aiming at the improvement of the prediction accuracy of current short-term traffic flow, a prediction model for short-term traffic flow based on cuckoo search algorithm-optimised wavelet neural network (CS-WNN) was presented.Firstly, wavelet transformation and normalisation were used for data noise reduction, and the phase space reconstruction of short-term traffic flow with chaotic characteristics was done to form training data set and test data set by using complex self-correlation algorithm.Then, the wavelet neural network whose parameters were first optimised by cuckoo search algorithm was trained with training data set.At last, test data set was used for validating the effectiveness of CS-WNN model.Simulation results show that compared with several mainstream optimised prediction models, the proposed CS-WNN model for short-term traffic flow prediction has higher prediction accuracy.关键词
短时交通流/复自相关/布谷鸟算法/小波神经网络Key words
Short-term traffic flow/Complex self-correlation/Cuckoo search algorithm/Wavelet neural network分类
信息技术与安全科学引用本文复制引用
黄晓慧,张翠芳..布谷鸟算法优化小波神经网络的短时交通流预测[J].计算机应用与软件,2017,34(3):238-242,5.
