Prediction of short-term transportation flow based on optimizing wavelet neural network by Genetic Algorithm

被引:2
作者
Yang Shenglong [1 ]
Ma Junjie [2 ,3 ]
Wang Cuihua [1 ]
Zhang Shengma [1 ]
机构
[1] Minist Agr, Key Lab East China Sea & Ocean Fishery Resources, Shanghai 200090, Peoples R China
[2] Tongji Univ, Sch Law, Shanghai, Peoples R China
[3] Tongji Univ, Sch Intellectual Property, Shanghai, Peoples R China
来源
MANUFACTURING PROCESS AND EQUIPMENT, PTS 1-4 | 2013年 / 694-697卷
基金
中国国家自然科学基金;
关键词
Wavelet neural network; Genetic Algorithm; Optimization;
D O I
10.4028/www.scientific.net/AMR.694-697.2715
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The weights and the parameter of Wavelet basis of the Wavelet neural network function are always initialized randomly, so the evolution of network tends to be local optima and each forecast results will vary widely. Genetic algorithm is used to optimal the weights and the parameter of Wavelet basis function of the Wavelet neural network, to construct a Wavelet neural network which is on the basis of genetic algorithm. In this paper, we apply this method to forecast short-term time traffic flow, verify with instances, and compare with Wavelet Neural Network Method. The results indicates that this method is not only more stable, but more precise.
引用
收藏
页码:2715 / +
页数:2
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