TY - GEN
T1 - Short-term Prediction of Hyperchaotic Flow Using Echo State Network
AU - Sinozaki, Aren
AU - Shiozawa, Kota
AU - Kajita, Kazuki
AU - Miyano, Takaya
AU - Horio, Yoshihiko
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.
PY - 2019/7
Y1 - 2019/7
N2 - 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.
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.
KW - echo state network
KW - hyperchaos
KW - reservoir computing
KW - time series prediction
UR - http://www.scopus.com/inward/record.url?scp=85073198555&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073198555&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2019.8852150
DO - 10.1109/IJCNN.2019.8852150
M3 - Conference contribution
AN - SCOPUS:85073198555
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2019 International Joint Conference on Neural Networks, IJCNN 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Joint Conference on Neural Networks, IJCNN 2019
Y2 - 14 July 2019 through 19 July 2019
ER -