An Online Learning Strategy for Echo State Network
受Woodbury矩阵恒等式启发，提出一种名为WOLESN的在线学习回声状态网络，支持逐个或逐块处理新数据，在时间序列预测和机器人轨迹预测上表现优异，代码已公开。
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 <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LongJin-lab/the-supplementary-file-for-WOLESN</uri> .