Next Article in Journal
TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System
Next Article in Special Issue
Optimizing Single DGX-A100 System: Overcoming GPU Limitations via Efficient Parallelism and Scheduling for Large Language Models
Previous Article in Journal
Privacy-Preserving and Explainable AI in Industrial Applications
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 12
Issue 13
10.3390/app12136396
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Novel Broad Echo State Network for Time Series Prediction: Cascade of Mapping Nodes and Optimization of Enhancement Layer

by
Wen-Jie Liu
1,2,3,
Yu-Ting Bai
1,2,3,*,
Xue-Bo Jin
1,2,3,*,
Jian-Lei Kong
1,2,3 and
Ting-Li Su
1,2,3
1
School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
2
Beijing Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China
3
State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2022, 12(13), 6396; https://doi.org/10.3390/app12136396
Submission received: 6 May 2022 / Revised: 20 June 2022 / Accepted: 22 June 2022 / Published: 23 June 2022
(This article belongs to the Special Issue Deep Learning for Data Analysis)
Browse Figures
Versions Notes

Abstract

Time series prediction is crucial for advanced control and management of complex systems, while the actual data are usually highly nonlinear and nonstationary. A novel broad echo state network is proposed herein for the prediction problem of complex time series data. Firstly, the framework of the broad echo state network with cascade of mapping nodes (CMBESN) is designed by embedding the echo state network units into the broad learning system. Secondly, the number of enhancement layer nodes of the CMBESN is determined by proposing an incremental algorithm. It can obtain the optimal network structure parameters. Meanwhile, an optimization method is proposed based on the nonstationary statistic metrics to determine the enhancement layer. Finally, experiments are conducted both on the simulated and actual datasets. The results show that the proposed CMBESN and its optimization have good prediction capability for nonstationary time series data.
Keywords: time series prediction; echo state networks; broad learning system; nonstationary analysis time series prediction; echo state networks; broad learning system; nonstationary analysis

Share and Cite

MDPI and ACS Style

Liu, W.-J.; Bai, Y.-T.; Jin, X.-B.; Kong, J.-L.; Su, T.-L. A Novel Broad Echo State Network for Time Series Prediction: Cascade of Mapping Nodes and Optimization of Enhancement Layer. Appl. Sci. 2022, 12, 6396. https://doi.org/10.3390/app12136396

AMA Style

Liu W-J, Bai Y-T, Jin X-B, Kong J-L, Su T-L. A Novel Broad Echo State Network for Time Series Prediction: Cascade of Mapping Nodes and Optimization of Enhancement Layer. Applied Sciences. 2022; 12(13):6396. https://doi.org/10.3390/app12136396

Chicago/Turabian Style

Liu, Wen-Jie, Yu-Ting Bai, Xue-Bo Jin, Jian-Lei Kong, and Ting-Li Su. 2022. "A Novel Broad Echo State Network for Time Series Prediction: Cascade of Mapping Nodes and Optimization of Enhancement Layer" Applied Sciences 12, no. 13: 6396. https://doi.org/10.3390/app12136396

APA Style

Liu, W.-J., Bai, Y.-T., Jin, X.-B., Kong, J.-L., & Su, T.-L. (2022). A Novel Broad Echo State Network for Time Series Prediction: Cascade of Mapping Nodes and Optimization of Enhancement Layer. Applied Sciences, 12(13), 6396. https://doi.org/10.3390/app12136396

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop