Short-term Prediction of Hyperchaotic Flow Using Echo State Network

Aren Sinozaki; Kota Shiozawa; Kazuki Kajita; Takaya Miyano; Yoshihiko Horio

doi:10.1109/IJCNN.2019.8852150

Short-term Prediction of Hyperchaotic Flow Using Echo State Network

Aren Sinozaki, Kota Shiozawa, Kazuki Kajita, Takaya Miyano, Yoshihiko Horio

Systems & Software Division

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

An echo state network with a reservoir consisting of 200 tanh neurons is applied to the short-term prediction of a chaotic time series generated using the augmented Lorenz equations as a hyperchaotic flow model. The predictive performance is examined in terms of the Kolmogorov-Sinai entropy and the Kaplan - Yorke dimension of a chaotic attractor in comparison with those for chaotic flow models having a single positive Lyapunov exponent. We discuss the predictive performance of the reservoir in terms of a universal simulator of chaotic attractors on the basis of Ueda's view of chaos, i.e., random transitions between unstable periodic orbits in a chaotic attractor.

Original language	English
Title of host publication	2019 International Joint Conference on Neural Networks, IJCNN 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728119854
DOIs	https://doi.org/10.1109/IJCNN.2019.8852150
Publication status	Published - 2019 Jul
Event	2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary Duration: 2019 Jul 14 → 2019 Jul 19

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2019-July

Conference

Conference	2019 International Joint Conference on Neural Networks, IJCNN 2019
Country/Territory	Hungary
City	Budapest
Period	19/7/14 → 19/7/19

Keywords

echo state network
hyperchaos
reservoir computing
time series prediction

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN.2019.8852150

Cite this

Sinozaki, A., Shiozawa, K., Kajita, K., Miyano, T., & Horio, Y. (2019). Short-term Prediction of Hyperchaotic Flow Using Echo State Network. In 2019 International Joint Conference on Neural Networks, IJCNN 2019 [8852150] (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2019.8852150

Short-term Prediction of Hyperchaotic Flow Using Echo State Network. / Sinozaki, Aren; Shiozawa, Kota; Kajita, Kazuki et al.

2019 International Joint Conference on Neural Networks, IJCNN 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8852150 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2019-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sinozaki, A, Shiozawa, K, Kajita, K, Miyano, T & Horio, Y 2019, Short-term Prediction of Hyperchaotic Flow Using Echo State Network. in 2019 International Joint Conference on Neural Networks, IJCNN 2019., 8852150, Proceedings of the International Joint Conference on Neural Networks, vol. 2019-July, Institute of Electrical and Electronics Engineers Inc., 2019 International Joint Conference on Neural Networks, IJCNN 2019, Budapest, Hungary, 19/7/14. https://doi.org/10.1109/IJCNN.2019.8852150

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note = "Funding Information: T.M. thanks Dr. Yoshisuke Ueda for many stimulating discussions. This study was partly supported by JSPS KAKENHI Grant Number JP18H03307. Funding Information: This study was partly supported by JSPS KAKENHI Grant Number 15K00353. XXX-X-XXXX-XXXX-X/XX/$XX.00 {\textcopyright}20XX IEEE Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 International Joint Conference on Neural Networks, IJCNN 2019 ; Conference date: 14-07-2019 Through 19-07-2019",

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N1 - Funding Information: T.M. thanks Dr. Yoshisuke Ueda for many stimulating discussions. This study was partly supported by JSPS KAKENHI Grant Number JP18H03307. Funding Information: This study was partly supported by JSPS KAKENHI Grant Number 15K00353. XXX-X-XXXX-XXXX-X/XX/$XX.00 ©20XX IEEE Publisher Copyright: © 2019 IEEE.

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AB - An echo state network with a reservoir consisting of 200 tanh neurons is applied to the short-term prediction of a chaotic time series generated using the augmented Lorenz equations as a hyperchaotic flow model. The predictive performance is examined in terms of the Kolmogorov-Sinai entropy and the Kaplan - Yorke dimension of a chaotic attractor in comparison with those for chaotic flow models having a single positive Lyapunov exponent. We discuss the predictive performance of the reservoir in terms of a universal simulator of chaotic attractors on the basis of Ueda's view of chaos, i.e., random transitions between unstable periodic orbits in a chaotic attractor.

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Short-term Prediction of Hyperchaotic Flow Using Echo State Network

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