Short-term Prediction of Hyperchaotic Flow Using Echo State Network
| Autor: | Yoshihiko Horio, Kazuki Kajita, Takaya Miyano, Kota Shiozawa, Aren Sinozaki |
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| Rok vydání: | 2019 |
| Předmět: |
Mathematics::Dynamical Systems
020208 electrical & electronic engineering Hyperbolic function Chaotic Reservoir computing 020206 networking & telecommunications 02 engineering and technology Lyapunov exponent Lorenz system Nonlinear Sciences::Chaotic Dynamics symbols.namesake Attractor 0202 electrical engineering electronic engineering information engineering symbols Applied mathematics Entropy (information theory) Echo state network Mathematics |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn.2019.8852150 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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