Sparse compressed deep echo state network with improved arithmetic optimization algorithm for chaotic time series prediction

A sparse compressed deep echo state network (SCDESN) incorporating sparse input units, compressed sampling and principal component analysis (PCA) units is proposed in this paper, and theoretically proved the sufficient and necessary conditions to ensure the echo state characteristics of the proposed...

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Bibliographic Details
Published in:Expert systems with applications Vol. 259; p. 125249
Main Authors: Wang, Hongbo, Mo, Yuanbin
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.01.2025
Subjects:
Arithmetic optimization algorithm
Chaotic time series prediction
Deep learning
Echo state networks
Hierarchical structure
Deep learning
Arithmetic optimization algorithm
Echo state networks
Chaotic time series prediction
Hierarchical structure
ISSN:0957-4174
Online Access:Get full text
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Summary:A sparse compressed deep echo state network (SCDESN) incorporating sparse input units, compressed sampling and principal component analysis (PCA) units is proposed in this paper, and theoretically proved the sufficient and necessary conditions to ensure the echo state characteristics of the proposed scheme. In addition, a hybrid arithmetic optimization algorithm based on matrix design strategy and boundary selection strategy (SVD-HAOA) was proposed to optimize the hyper-parameters of the SCDESN model, taking into account the characteristics of the SCDESN model. The steps and process for optimizing the hyper parameters of the SCDESN model using SVD-HAOA were presented. Finally, the SCDESN model optimized by SVD-HAOA was experimentally and summarized on a classic benchmark dataset and two real-world chaotic time series. The outcomes of the simulation indicated that the suggested model surpassed alternative models in performance and enhanced computational efficiency while maintaining prediction accuracy. This model may serve as a viable alternative for real-world applications.
ISSN:0957-4174
DOI:10.1016/j.eswa.2024.125249