Neural Echo State Network using oscillations of gas bubbles in water: Computational validation by Mackey-Glass time series forecasting

• URL: http://arxiv.org/abs/2112.11592v1
• Date: Wed, 22 Dec 2021 00:21:54 GMT
• Title: Neural Echo State Network using oscillations of gas bubbles in water: Computational validation by Mackey-Glass time series forecasting
• Authors: Ivan S. Maksymov and Andrey Pototsky and Sergey A. Suslov
• Abstract summary: We propose an RC system that combines the nonlinearity of an acoustic response of a cluster of oscillating gas bubbles in water with a standard Echo State Network (ESN) algorithm. We computationally confirm the plausibility of the proposed RC system by demonstrating its ability to forecast a chaotic Mackey-Glass time series with the efficiency of ESN.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physical reservoir computing (RC) is a computational framework, where machine learning algorithms designed for digital computers are executed using analog computer-like nonlinear physical systems that can provide high computational power for predicting time-dependent quantities that can be found using nonlinear differential equations. Here we suggest an RC system that combines the nonlinearity of an acoustic response of a cluster of oscillating gas bubbles in water with a standard Echo State Network (ESN) algorithm that is well-suited to forecast nonlinear and chaotic time series. We computationally confirm the plausibility of the proposed RC system by demonstrating its ability to forecast a chaotic Mackey-Glass time series with the efficiency of ESN.

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