February 8, 2024Open Access

Physical Reservoir Computing Enabled by Solitary Waves and Biologically Inspired Nonlinear Transformation of Input Data

Key Points

The physical reservoir computing system effectively forecasts chaotic time series, enhancing prediction accuracy with limited processing power.
Key evidence confirms that the innovative approach utilizes solitary waves to perform nonlinear transformations, achieving successful results across tests.
Experimental validation included operations with a microcontroller, demonstrating practicality for the next-generation hardware counterpart to traditional reservoir computing algorithms. This enhances accessibility for real-world applications by minimizing computational demand through biological inspiration.

Abstract

Reservoir computing (RC) systems can efficiently forecast chaotic time series using the nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more-efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper, we experimentally validated a physical RC system that substitutes the effect of randomness that underpins the operation of the traditional RC algorithm for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the ‘next-generation’ improvement of the traditional RC algorithm.

Physical Reservoir Computing Enabled by Solitary Waves and Biologically Inspired Nonlinear Transformation of Input Data

Key Points

Abstract

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