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Neural Computing and Applications
Published in: Neural Computing and Applications 10/2019

04-05-2018 | Original Article

Dynamical regularized echo state network for time series prediction

Authors: Cuili Yang, Junfei Qiao, Lei Wang, Xinxin Zhu

Published in: Neural Computing and Applications | Issue 10/2019

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Abstract

Echo state networks (ESNs) have been widely used in the field of time series prediction. However, it is difficult to automatically determine the structure of ESN for a given task. To solve this problem, the dynamical regularized ESN (DRESN) is proposed. Different from other growing ESNs whose existing architectures are fixed when new reservoir nodes are added, the current component of DRESN may be replaced by the newly generated network with more compact structure and better prediction performance. Moreover, the values of output weights in DRESN are updated by the error minimization-based method, and the norms of output weights are controlled by the regularization technique to prevent the ill-posed problem. Furthermore, the convergence analysis of the DRESN is given theoretically and experimentally. Simulation results demonstrate that the proposed approach can have few reservoir nodes and better prediction accuracy than other existing ESN models.
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Appendix
Available only for authorised users
Footnotes
1
The mathematical operation fix(A) is to round the value of A to the nearest integer toward zero.
 
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Metadata
Title
Dynamical regularized echo state network for time series prediction
Authors
Cuili Yang
Junfei Qiao
Lei Wang
Xinxin Zhu
Publication date
04-05-2018
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3488-z

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