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Transfer-RLS method and transfer-FORCE learning for simple and fast training of reservoir computing models.

Hiroto Tamura1, Gouhei Tanaka1

  • 1Graduate School of Engineering, The University of Tokyo, 113-8656 Tokyo, Japan; International Research Center for Neurointelligence, The University of Tokyo, 113-0033 Tokyo, Japan.

Neural Networks : the Official Journal of the International Neural Network Society
|July 25, 2021
PubMed
Summary

This study introduces transfer-RLS, a novel training method for reservoir computing that balances fast convergence with simpler operations. It offers an efficient alternative to existing methods for low-power, real-time machine learning hardware.

Keywords:
FORCE learningOnline supervised learningRecurrent neural networksRecursive least squares methodReservoir computing

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Area of Science:

  • * Computational neuroscience and machine learning.
  • * Development of energy-efficient hardware for artificial intelligence.

Background:

  • * Reservoir computing, a type of recurrent neural network, shows promise for energy-efficient, real-time information processing hardware.
  • * Existing online learning methods like Recursive Least Squares (RLS) and Least Mean Squares (LMS) present trade-offs between convergence speed and computational complexity.
  • * Efficient online learning is crucial for low-power reservoir computing devices.

Purpose of the Study:

  • * To develop a novel, efficient online learning method for reservoir computing models.
  • * To bridge the performance gap between RLS and LMS methods.
  • * To enable simpler operations and faster convergence for training reservoir computing hardware.

Main Methods:

  • * Proposal of the transfer-RLS method, a hybrid approach between RLS and LMS.
  • * Utilizing a pre-training phase to update the gain matrix, avoiding updates during main training.
  • * Numerical and analytical validation of the transfer-RLS method's performance.
  • * Development of a modified transfer-RLS method (transfer-FORCE learning) for closed-loop systems.

Main Results:

  • * The transfer-RLS method demonstrates significantly faster convergence than the LMS method.
  • * It achieves comparable convergence speed to RLS with substantially simpler operations.
  • * The transfer-FORCE learning variant successfully applies to challenging closed-loop reservoir computing models.
  • * The proposed method offers a practical solution for efficient training of physical reservoir computing hardware.

Conclusions:

  • * The transfer-RLS method provides an effective and computationally efficient approach for online learning in reservoir computing.
  • * This method enhances the feasibility of using low-power reservoir computing devices for real-time applications.
  • * The transfer-FORCE learning extension broadens the applicability of reservoir computing to more complex dynamic systems.