Short-Term Prediction Of Hyperchaotic Flow Using Echo State Network
2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)(2019)
摘要
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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关键词
reservoir computing, echo state network, time series prediction, hyperchaos
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