Short Term Traffic Flow Forecasting Based on Improved Echo State Network

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	Short Term Traffic Flow Forecasting Based on Improved Echo State Network
	Cao, Jie; Yu, Da-Wei; Hou, Liang
	2016
关键词	Short Term Traffic Flow Forecast Echo State Network Reservoirs Topological Structure
页码	679-688
英文摘要	The echo state network reserve pool is a random connection between the neurons, which makes the strong coupling between the neurons limit the richness of neuron dynamics, impacting prediction accuracy. In view of the above problems, a new echo state network with the characteristics of world small is proposed. Small world topology is generated by using a new algorithm based on neuron space growth, then the nodes in the network are sorted in a new way, finally, the connection between the physical nodes in the network and their interaction is mapped to the inner neurons of the new echo state network reserve pool. Simulation experiments show that, the dynamic characteristics of the improved echo state network are more abundant than the original ESN, and the accuracy of the prediction is better than that of the conventional ESN.
会议录	2016 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION SECURITY (CSIS 2016)
会议录出版者	DESTECH PUBLICATIONS, INC
会议录出版地	439 DUKE STREET, LANCASTER, PA 17602-4967 USA
语种	英语
WOS研究方向	Computer Science
WOS记录号	WOS:000389852900098
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/36327]
专题	兰州理工大学计算机与通信学院
通讯作者	Yu, Da-Wei
作者单位	Lanzhou Univ Technol, Coll Comp & Commun, Lanzhou 730050, Peoples R China
推荐引用方式 GB/T 7714	Cao, Jie,Yu, Da-Wei,Hou, Liang. Short Term Traffic Flow Forecasting Based on Improved Echo State Network[C]. 见:.