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Related Experiment Video

Updated: Nov 11, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

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Transfer learning of chaotic systems.

Yali Guo1, Han Zhang1, Liang Wang1

  • 1School of Physics and Information Technology, Shaanxi Normal University, Xi'an 710062, China.

Chaos (Woodbury, N.Y.)
|March 23, 2021
PubMed
Summary
This summary is machine-generated.

Transfer learning for chaotic systems is explored using reservoir computers. Knowledge transfer is successful when systems differ in parameters but fails when dynamics differ, though chained systems show promise.

Related Experiment Videos

Last Updated: Nov 11, 2025

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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Published on: May 8, 2021

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Area of Science:

  • Complex Systems
  • Machine Learning
  • Nonlinear Dynamics

Background:

  • Transfer learning, crucial in machine learning, has not been extensively studied for chaotic systems.
  • Predicting the evolution of one system using models trained on another is a key challenge.

Purpose of the Study:

  • Investigate transfer learning for chaotic systems using synchronization-based state inference.
  • Determine the feasibility of using a reservoir computer trained on one chaotic system to predict another.

Main Methods:

  • Employed reservoir computing, a type of recurrent neural network, for time-series prediction.
  • Trained reservoir computers on a source chaotic system (A) and tested their ability to infer states of a target chaotic system (B).
  • Varied the differences between systems A and B in terms of parameters and dynamics.

Main Results:

  • Successful synchronization and state inference when systems A and B differed only in parameters.
  • General failure of synchronization when systems A and B differed in dynamics.
  • Effective knowledge transfer through a chain of coupled reservoir computers, inferring remote system states.
  • Demonstrated practical application using a chaotic pendulum experiment.

Conclusions:

  • Transfer learning in chaotic systems is parameter-dependent; successful if only parameters differ.
  • Chained reservoir computers enable knowledge transfer even with differing system dynamics.
  • The study validates the transferability of learned dynamics from modeling to experimental chaotic systems.