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Chaotic Time Series Prediction of Multi‐Dimensional Nonlinear System Based on Bidirectional LSTM Model

Chaotic Time Series Prediction of Multi‐Dimensional Nonlinear System Based on Bidirectional LSTM... The current work proposes a hybrid data‐driven model—Convolutional bidirectional long–short term memory (CNN‐BLSTM) for predicting chaotic behavior of three‐coupled Duffing oscillator nonlinear system, in which the CNN is for efficiently extracting the more robust and informative representations of chaotic sequences while the BLSTM is for holding the long‐term dependencies combining the past and future contexts. Different from traditional analytical and numerical approaches, the proposed prediction model features the benefit of focusing on the measured data solely without extensive professional domain knowledge. Additionally, three more recurrent neural network (RNN) models, including simple RNNs, stack LSTMs, and BLSTM, are built and comparisons of generalization performances to the CNN‐BLSTM are conducted. From the findings so far, the CNN‐BLSTM is able to learn the pattern of chaotic time sequence data with less training time and apply the acquired knowledge to the unseen dataset with lower errors. Moreover, the current work decently demonstrates that the proposed model outperforms other three models in terms of stability at different noise levels from two evaluation criteria. The CNN‐BLSTM provides useful guidance for the consideration of predicting multi‐dimensional nonlinear chaotic behavior. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Advanced Theory and Simulations Wiley

Chaotic Time Series Prediction of Multi‐Dimensional Nonlinear System Based on Bidirectional LSTM Model

Wang, Luyao; Dai, Liming
Advanced Theory and Simulations , Volume 6 (8) – Aug 1, 2023
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References (48)

Publisher
Wiley
Copyright
© 2023 Wiley‐VCH GmbH
eISSN
2513-0390
DOI
10.1002/adts.202300148
Publisher site
See Article on Publisher Site

Abstract

The current work proposes a hybrid data‐driven model—Convolutional bidirectional long–short term memory (CNN‐BLSTM) for predicting chaotic behavior of three‐coupled Duffing oscillator nonlinear system, in which the CNN is for efficiently extracting the more robust and informative representations of chaotic sequences while the BLSTM is for holding the long‐term dependencies combining the past and future contexts. Different from traditional analytical and numerical approaches, the proposed prediction model features the benefit of focusing on the measured data solely without extensive professional domain knowledge. Additionally, three more recurrent neural network (RNN) models, including simple RNNs, stack LSTMs, and BLSTM, are built and comparisons of generalization performances to the CNN‐BLSTM are conducted. From the findings so far, the CNN‐BLSTM is able to learn the pattern of chaotic time sequence data with less training time and apply the acquired knowledge to the unseen dataset with lower errors. Moreover, the current work decently demonstrates that the proposed model outperforms other three models in terms of stability at different noise levels from two evaluation criteria. The CNN‐BLSTM provides useful guidance for the consideration of predicting multi‐dimensional nonlinear chaotic behavior.

Journal

Advanced Theory and Simulations – Wiley

Published: Aug 1, 2023

Keywords: bidirectional long–short term memory; chaos; Convolutional Neural Network; multi‐dimensional nonlinear system

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