Efficient Mini-batch Training for Echo State Networks
Authors: 
Chunyuan Zhang, Chao Liu, Jie ZhaoAuthors Info & Claims
Chunyuan Zhang, Chao Liu, Jie ZhaoAuthors Info & ClaimsPages 236 - 240
References
[1]
Jaeger, H. 2001. The “echo state” approach to analyzing and training recurrent neural networks. Bonn: German National Research Institute for Computer Science, 148 (Jan. 2001), 1-43.
[2]
Jaeger, H. and Haas, H. 2004. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science, 304, 5667 (Apr. 2004), 78-80. DOI= 10.1126/SCIENCE.1091277.
[3]
Skowronski, M. D. and Harris, J. G. 2007. Automatic speech recognition using a predictive echo state network classifier. Neural Networks, 20, 3 (Apr. 2007), 414-423. DOI= 10.1016/J.NEUNET.2007.04.006.
[4]
Zhang, B. 2017. Short-term stock price forecast model based on echo state network. Computer Applications and Software, 34, 5 (May. 2017), 268-272. DOI= 10.3969/j.issn.1000-386x. 2017.05.046
[5]
Shi, Z. W. and Han, M. 2007. Ridge regression learning in ESN for chaotic time series prediction. Control and Decision, 22, 3 (Mar. 2007), 258-261. DOI= CNKI:SUN:KZYC.0. 2007-03-003
[6]
Jaeger, H. 2003. Adaptive nonlinear system identification with echo state networks. In Proc of the 15th Int Conf on Neural Information Processing Systems (Cambridge, MA, USA). MIT Press, Cambridge, 609-616.
[7]
Song, Q., Zhao, X., and Feng, Z. 2011. Improved recursive least squares algorithm based on echo state neural network for nonlinear system identification. In Proc of the 30th Chinese Control Conf (Yantai, China, July 22-24, 2011). IEEE, 1692-1695.
[8]
Goodfellow, I., Bengio Y., and Courville, A. 2016. Deep learning. MIT Press.
[9]
Yang, C., Qiao, J., Ahmad, Z., Nie, K., and Wang, L. 2019. Online sequential echo state network with sparse RLS algorithm for time series prediction. Neural Networks, 118, 10(May.2019),32-42.DOI= 10.1016/J.NEUNET.2019.05.006.
[10]
Gawlik, D. 2020. New York Stock Exchange. April 10, 2020 from https://www.kaggle.com/dgawlik/nyse.
[11]
Jaeger, H. 2002. A tutorial on training recurrent neural networks, covering BPPT, RTRL, EKF and the “echo state network” approach. Bonn: German National Research Institute for Computer Science, 5 (Jan. 2002), 1-46.
[12]
Jaeger, H., Lukǒsevic̆ius, M., and Popovici, D. 2007. Optimization and applications of echo state networks with leaky integrator neurons. Neural Networks, 20, 3 (Apr. 2007), 335-352. DOI= 10.1016/J.NEUNET.2007.04.016.
[13]
Sherman, J., Morrison, and W. J. 1950. Adjustment of an inverse matrix corresponding to a change in one element of a given matrix. The Annals of Mathematical Statistics, 21, 1, 124-127. DOI= 10.1214/AOMS/1177729893.
[14]
Bishop, C. M. 1995. Neural networks for pattern recognition. Oxford university press, Oxford, 153.
[15]
Ekşioǧlu, E. M. 2010. RLS adaptive filtering with sparsity regularization. In Proc of 10th Int Conf on Information Science, Signal Processing and their Applications (Kuala Lumpur, Malaysia, May 10-13, 2010). IEEE, New York, NY, 550-553. DOI= 10.1109/ISSPA.2010.5605592.
[16]
Paleologu, C., Benesty, J., and Ciochina, S. 2008. A robust variable forgetting factor recursive least-squares algorithm for system identification. IEEE Signal Processing Letters, 15 (May. 2008), 597-600. DOI= 10.1109/LSP.2008.2001559.
[17]
Albu, F. 2012. Improved variable forgetting factor recursive least square algorithm. In 12th Int Conf on Control Automation Robotics & Vision (Guangzhou, China, December 05-07, 2012). IEEE, 1789-1793. DOI= 10.1109/ICARCV.2012.6485421.
Index Terms
- Efficient Mini-batch Training for Echo State Networks
Index terms have been assigned to the content through auto-classification.
Recommendations
Anti Boundary Effect Wavelet Decomposition Echo State Networks
8th International Symposium on Advances in Neural Networks --- ISNN 2011 - Volume 6675We propose a novel approach based on wavelet decomposition and echo state networks to discover the multiscale dynamics of time series which we call anti-boundary-effect wavelet decomposition and echo state networks ABE-WESNs. ABE-WESNs use the wavelet ...
Rapid training echo convolution network for image recognition
AbstractConvolutional Neural Network (CNN) is highly effective in image recognition tasks, however, the fully connected layers account for over 90% of the network's parameters. To address this issue, we propose a novel method, the Rapid Training Echo ...
Highlights- Introduces M-ESN: a multi-layer deep echo state network with parallel sub-reservoirs.
- Reduces neural connections by using representative neurons and virtual reservoirs.
- Replaces CNN's fully connected layer with M-ESN, forming RT-...
Anti boundary effect wavelet decomposition echo state networks
ISNN'11: Proceedings of the 8th international conference on Advances in neural networks - Volume Part IWe propose a novel approach based on wavelet decomposition and echo state networks to discover the multiscale dynamics of time series which we call anti-boundary-effect wavelet decomposition and echo state networks (ABE-WESNs). ABE-WESNs use the wavelet ...
Comments
Information & Contributors
Information
Published In
November 2020
288 pages
ISBN:9781450388597
DOI:10.1145/3449301
Copyright © 2020 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 June 2021
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- the National Natural Science Foundation of China
Conference
ICRAI 2020
ICRAI 2020: 2020 6th International Conference on Robotics and Artificial Intelligence
November 20 - 22, 2020
Singapore, Singapore
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 38Total Downloads
- Downloads (Last 12 months)5
- Downloads (Last 6 weeks)0
Reflects downloads up to 28 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format