Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO

Xiaodong Zhang; Xuefeng Yan

doi:doi:10.11648/j.acis.20160406.11

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Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO

Xiaodong Zhang, Xuefeng Yan

Published in Automation, Control and Intelligent Systems (Volume 4, Issue 6)

Received: 29 November 2016 Accepted: Published: 1 December 2016

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Abstract

The Echo State Network (ESN) is a novel and special type of recurrent neural network that has become increasingly popular in machine learning domains such as time series forecasting, data clustering, and nonlinear system identification. This network is characterized by large randomly constructed recurrent neural networks (RNN) called “reservoir”, in which the neurons are sparsely connected and the weights remain unchanged during training, leaving the simple training of the output layer. However, the reservoir is criticized for its randomness and instability because of the random initialization of the connectivity and weights. In this article, we introduced the selective ensemble learning based on BPSO to improve the generalization performance of ESN. Two widely studied tasks are used to prove the feasibility and priority of the selective ESN ensemble based on BPSO(SESNE-BPSO) model. And the results indicate that the SESNE-BPSO model performs better than the general ESN ensemble, the single standard ESN and several other improved ESN models.

Published in	Automation, Control and Intelligent Systems (Volume 4, Issue 6)
DOI	10.11648/j.acis.20160406.11
Page(s)	84-88
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Echo State Network, Reservoir Computing, Artificial Neural Network, Ensemble Learning, Selective Ensemble, Particle Swarm Optimization

References

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APA Style

Xiaodong Zhang, Xuefeng Yan. (2016). Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO. Automation, Control and Intelligent Systems, 4(6), 84-88. https://doi.org/10.11648/j.acis.20160406.11

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ACS Style

Xiaodong Zhang; Xuefeng Yan. Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO. Autom. Control Intell. Syst. 2016, 4(6), 84-88. doi: 10.11648/j.acis.20160406.11

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AMA Style

Xiaodong Zhang, Xuefeng Yan. Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO. Autom Control Intell Syst. 2016;4(6):84-88. doi: 10.11648/j.acis.20160406.11

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@article{10.11648/j.acis.20160406.11,
  author = {Xiaodong Zhang and Xuefeng Yan},
  title = {Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO},
  journal = {Automation, Control and Intelligent Systems},
  volume = {4},
  number = {6},
  pages = {84-88},
  doi = {10.11648/j.acis.20160406.11},
  url = {https://doi.org/10.11648/j.acis.20160406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20160406.11},
  abstract = {The Echo State Network (ESN) is a novel and special type of recurrent neural network that has become increasingly popular in machine learning domains such as time series forecasting, data clustering, and nonlinear system identification. This network is characterized by large randomly constructed recurrent neural networks (RNN) called “reservoir”, in which the neurons are sparsely connected and the weights remain unchanged during training, leaving the simple training of the output layer. However, the reservoir is criticized for its randomness and instability because of the random initialization of the connectivity and weights. In this article, we introduced the selective ensemble learning based on BPSO to improve the generalization performance of ESN. Two widely studied tasks are used to prove the feasibility and priority of the selective ESN ensemble based on BPSO(SESNE-BPSO) model. And the results indicate that the SESNE-BPSO model performs better than the general ESN ensemble, the single standard ESN and several other improved ESN models.},
 year = {2016}
}

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TY  - JOUR
T1  - Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO
AU  - Xiaodong Zhang
AU  - Xuefeng Yan
Y1  - 2016/12/01
PY  - 2016
N1  - https://doi.org/10.11648/j.acis.20160406.11
DO  - 10.11648/j.acis.20160406.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 84
EP  - 88
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20160406.11
AB  - The Echo State Network (ESN) is a novel and special type of recurrent neural network that has become increasingly popular in machine learning domains such as time series forecasting, data clustering, and nonlinear system identification. This network is characterized by large randomly constructed recurrent neural networks (RNN) called “reservoir”, in which the neurons are sparsely connected and the weights remain unchanged during training, leaving the simple training of the output layer. However, the reservoir is criticized for its randomness and instability because of the random initialization of the connectivity and weights. In this article, we introduced the selective ensemble learning based on BPSO to improve the generalization performance of ESN. Two widely studied tasks are used to prove the feasibility and priority of the selective ESN ensemble based on BPSO(SESNE-BPSO) model. And the results indicate that the SESNE-BPSO model performs better than the general ESN ensemble, the single standard ESN and several other improved ESN models.
VL  - 4
IS  - 6
ER  -

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Author Information

Xiaodong Zhang

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, P. R. China
Xuefeng Yan

Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, P. R. China

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APA Style

Xiaodong Zhang, Xuefeng Yan. (2016). Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO. Automation, Control and Intelligent Systems, 4(6), 84-88. https://doi.org/10.11648/j.acis.20160406.11

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ACS Style

Xiaodong Zhang; Xuefeng Yan. Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO. Autom. Control Intell. Syst. 2016, 4(6), 84-88. doi: 10.11648/j.acis.20160406.11

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AMA Style

Xiaodong Zhang, Xuefeng Yan. Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO. Autom Control Intell Syst. 2016;4(6):84-88. doi: 10.11648/j.acis.20160406.11

Copy | Download

@article{10.11648/j.acis.20160406.11,
  author = {Xiaodong Zhang and Xuefeng Yan},
  title = {Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO},
  journal = {Automation, Control and Intelligent Systems},
  volume = {4},
  number = {6},
  pages = {84-88},
  doi = {10.11648/j.acis.20160406.11},
  url = {https://doi.org/10.11648/j.acis.20160406.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acis.20160406.11},
  abstract = {The Echo State Network (ESN) is a novel and special type of recurrent neural network that has become increasingly popular in machine learning domains such as time series forecasting, data clustering, and nonlinear system identification. This network is characterized by large randomly constructed recurrent neural networks (RNN) called “reservoir”, in which the neurons are sparsely connected and the weights remain unchanged during training, leaving the simple training of the output layer. However, the reservoir is criticized for its randomness and instability because of the random initialization of the connectivity and weights. In this article, we introduced the selective ensemble learning based on BPSO to improve the generalization performance of ESN. Two widely studied tasks are used to prove the feasibility and priority of the selective ESN ensemble based on BPSO(SESNE-BPSO) model. And the results indicate that the SESNE-BPSO model performs better than the general ESN ensemble, the single standard ESN and several other improved ESN models.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Improvement of Echo State Network Generalization by Selective Ensemble Learning Based on BPSO
AU  - Xiaodong Zhang
AU  - Xuefeng Yan
Y1  - 2016/12/01
PY  - 2016
N1  - https://doi.org/10.11648/j.acis.20160406.11
DO  - 10.11648/j.acis.20160406.11
T2  - Automation, Control and Intelligent Systems
JF  - Automation, Control and Intelligent Systems
JO  - Automation, Control and Intelligent Systems
SP  - 84
EP  - 88
PB  - Science Publishing Group
SN  - 2328-5591
UR  - https://doi.org/10.11648/j.acis.20160406.11
AB  - The Echo State Network (ESN) is a novel and special type of recurrent neural network that has become increasingly popular in machine learning domains such as time series forecasting, data clustering, and nonlinear system identification. This network is characterized by large randomly constructed recurrent neural networks (RNN) called “reservoir”, in which the neurons are sparsely connected and the weights remain unchanged during training, leaving the simple training of the output layer. However, the reservoir is criticized for its randomness and instability because of the random initialization of the connectivity and weights. In this article, we introduced the selective ensemble learning based on BPSO to improve the generalization performance of ESN. Two widely studied tasks are used to prove the feasibility and priority of the selective ESN ensemble based on BPSO(SESNE-BPSO) model. And the results indicate that the SESNE-BPSO model performs better than the general ESN ensemble, the single standard ESN and several other improved ESN models.
VL  - 4
IS  - 6
ER  -

Copy | Download