학술논문
Composite FORCE Learning of Chaotic Echo State Networks for Time-Series Prediction
Document Type
Conference
Author
Source
2022 41st Chinese Control Conference (CCC) Chinese Control Conference, 2022 41st. :7355-7360 Jul, 2022
Subject
Language
ISSN
1934-1768
Abstract
Echo state network (ESN), a kind of recurrent neural networks, consists of a fixed reservoir in which neurons are connected randomly and recursively and obtains the desired output only by training output connection weights. First-order reduced and controlled error (FORCE) learning is an online supervised training approach that can change the chaotic activity of ESNs into specified activity patterns. This paper proposes a composite FORCE learning method based on recursive least squares to train ESNs whose initial activity is spontaneously chaotic, where a composite learning technique featured by dynamic regressor extension and memory data exploitation is applied to enhance parameter convergence. The proposed method is applied to a benchmark problem about predicting chaotic time series generated by the Mackey-Glass system, and numerical results have shown that it significantly improves learning and prediction performances compared with existing methods.