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Neural Computing and Applications

Erschienen in:

04.05.2018 | Original Article

Dynamical regularized echo state network for time series prediction

verfasst von: Cuili Yang, Junfei Qiao, Lei Wang, Xinxin Zhu

Erschienen in: Neural Computing and Applications | Ausgabe 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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Titel: Dynamical regularized echo state network for time series prediction
verfasst von: Cuili Yang
Junfei Qiao
Lei Wang
Xinxin Zhu
Publikationsdatum: 04.05.2018
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 10/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3488-z

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