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Time-shift selection for reservoir computing using a rank-revealing QR algorithm.

Joseph D Hart1, Francesco Sorrentino2, Thomas L Carroll1

  • 1US Naval Research Laboratory, Washington, DC 20375, USA.

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Summary
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This study introduces a novel method for optimizing reservoir computing by selecting time-shifts to enhance accuracy. This technique improves performance in nonlinear system prediction and control tasks.

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Area of Science:

  • Computational neuroscience
  • Machine learning
  • Nonlinear dynamics

Background:

  • Reservoir computing (RC) is a powerful recurrent neural network approach for nonlinear system analysis.
  • Incorporating time-shifts in reservoir signals has shown potential for improving RC performance.
  • Optimal selection of these time-shifts remains a challenge.

Purpose of the Study:

  • To develop a systematic, model-independent technique for selecting optimal time-shifts in reservoir computing.
  • To enhance the performance accuracy of reservoir computers, particularly for analog hardware implementations.
  • To demonstrate the efficacy of the proposed method across different reservoir computing architectures.

Main Methods:

  • A novel time-shift selection technique is proposed, based on maximizing the rank of the reservoir matrix.
  • A rank-revealing QR algorithm is employed to identify optimal time-shifts.
  • The method is validated on an optoelectronic reservoir computer and a traditional recurrent neural network with tanh activation.

Main Results:

  • The proposed technique effectively selects time-shifts by maximizing reservoir matrix rank.
  • Improved accuracy was observed compared to random time-shift selection across tested reservoir computer types.
  • The method's task-independent nature makes it broadly applicable, including to analog hardware.

Conclusions:

  • The developed time-shift selection method offers a robust and effective way to enhance reservoir computing performance.
  • This approach is directly applicable to physical reservoir computing systems without requiring system models.
  • The findings suggest a significant advancement in optimizing reservoir computing for complex nonlinear tasks.