Model-Free Prediction of Chaotic Systems Using High Efficient Next-generation Reservoir Computing

• URL: http://arxiv.org/abs/2110.13614v1
• Date: Tue, 19 Oct 2021 12:49:24 GMT
• Title: Model-Free Prediction of Chaotic Systems Using High Efficient Next-generation Reservoir Computing
• Authors: Zhuo Liu and Leisheng Jin
• Abstract summary: A new paradigm of reservoir computing is proposed for achieving model-free predication for both low-dimensional and large chaotic systems. By taking the Lorenz and Kuramoto-Sivashinsky equations as two classical examples of dynamical systems, numerical simulations are conducted. The results show our model excels at predication tasks than the latest reservoir computing methods.
• Score: 4.284497690098487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To predict the future evolution of dynamical systems purely from observations of the past data is of great potential application. In this work, a new formulated paradigm of reservoir computing is proposed for achieving model-free predication for both low-dimensional and very large spatiotemporal chaotic systems. Compared with traditional reservoir computing models, it is more efficient in terms of predication length, training data set required and computational expense. By taking the Lorenz and Kuramoto-Sivashinsky equations as two classical examples of dynamical systems, numerical simulations are conducted, and the results show our model excels at predication tasks than the latest reservoir computing methods.

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