An Online Learning Strategy for Echo State Network | IEEE Journals & Magazine | IEEE Xplore

An Online Learning Strategy for Echo State Network


Abstract:

As an effective alternative to recurrent neural networks, the echo state network (ESN) has achieved great success. However, the commonly-used batch learning-based algorit...Show More

Abstract:

As an effective alternative to recurrent neural networks, the echo state network (ESN) has achieved great success. However, the commonly-used batch learning-based algorithms prevent the ESN from being able to learn and train online. In this article, inspired by the Woodbury matrix identity, an online learning ESN named Woodbury online learning ESN (WOLESN) is proposed, which allows new data to arrive in a one-by-one or block-by-block manner. Experiments on the benchmark datasets of time series prediction and comparison models verify the effectiveness and superiority of the WOLESN. In addition, observing the relationship between the time series prediction and robot control, experiments on the redundant manipulator are designed with the aid of the proposed WOLESN, of which results indicate that the WOLESN does an excellent job of predicting the trajectory of the robot with tiny errors. The code of WOLESN is publicly available at https://github.com/LongJin-lab/the-supplementary-file-for-WOLESN.
Published in: IEEE Transactions on Systems, Man, and Cybernetics: Systems ( Volume: 54, Issue: 1, January 2024)
Page(s): 644 - 655
Date of Publication: 12 October 2023

ISSN Information:

Funding Agency:


References

References is not available for this document.