Short-term Prediction of Hyperchaotic Flow Using Echo State Network

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

研究成果: Conference contribution

抄録

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.

本文言語English
ホスト出版物のタイトル2019 International Joint Conference on Neural Networks, IJCNN 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728119854
DOI
出版ステータスPublished - 2019 7
イベント2019 International Joint Conference on Neural Networks, IJCNN 2019 - Budapest, Hungary
継続期間: 2019 7 142019 7 19

出版物シリーズ

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

Conference

Conference2019 International Joint Conference on Neural Networks, IJCNN 2019
CountryHungary
CityBudapest
Period19/7/1419/7/19

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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