Proceedings of the 2nd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2016)

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

Authors
Guan-Fang Wu, Hong-Yan Cui
Corresponding Author
Guan-Fang Wu
Available Online December 2016.
DOI
10.2991/eeeis-16.2017.105How to use a DOI?
Keywords
Echo State Network; Prediction; Structure Improvement; Modelling capability analysis
Abstract

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.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2016)
Series
Advances in Engineering Research
Publication Date
December 2016
ISBN
10.2991/eeeis-16.2017.105
ISSN
2352-5401
DOI
10.2991/eeeis-16.2017.105How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Guan-Fang Wu
AU  - Hong-Yan Cui
PY  - 2016/12
DA  - 2016/12
TI  - A survey of the past, present and future of echo state networks
BT  - Proceedings of the 2nd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 2016)
PB  - Atlantis Press
SP  - 850
EP  - 861
SN  - 2352-5401
UR  - https://doi.org/10.2991/eeeis-16.2017.105
DO  - 10.2991/eeeis-16.2017.105
ID  - Wu2016/12
ER  -