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

by 1,2,3, 1,2,3,*, 1,2,3,*, 1,2,3 and 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.
Academic Editors: Andres Alvarez-Meza and David Cárdenas-Peña
Appl. Sci. 2022, 12(13), 6396; https://doi.org/10.3390/app12136396
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)
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. View Full-Text
Keywords: time series prediction; echo state networks; broad learning system; nonstationary analysis time series prediction; echo state networks; broad learning system; nonstationary analysis
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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

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