A survey of the past, present and future of echo state networks

被引：0
|
作者：
Wu, Guan-Fang [1 ]
Cui, Hong-Yan [1 ]
机构：
[1] Beijing Univ Post & Telecommun, Sch Informat & Telecommun, State Key Lab Networking & Switching Technol, Key Lab Network Syst Architecture & Convergence, Beijing 100876, Peoples R China
来源：
PROCEEDINGS OF THE 2ND ANNUAL INTERNATIONAL CONFERENCE ON ELECTRONICS, ELECTRICAL ENGINEERING AND INFORMATION SCIENCE (EEEIS 2016) | 2016年 / 117卷
基金：
中国国家自然科学基金;
关键词：
Echo State Network; Prediction; Structure Improvement; Modelling capability analysis; READOUT;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
Along with the development of Machine Learning, statistic and Artificial Intelligence, people are exposed to myriad of big data. Meanwhile, accurate data analysis is difficult. Echo state network (ESN) algorithms are widely researched and applied in many fields. Owing to their potential for exact prediction and simple training process, scientists pay more attention to the research of ESN. In this paper, the representative research is carried out to sum up the research achievements on ESN, and the future development direction is discussed by pointing out the key technical challenges and we suggest several strategies for tackling the challenges.
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页码：850 / 861
页数：12
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