Matches in SemOpenAlex for { <https://semopenalex.org/work/W4312779997> ?p ?o ?g. }
- W4312779997 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." @default.
- W4312779997 created "2023-01-05" @default.
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- W4312779997 date "2022-07-25" @default.
- W4312779997 modified "2023-10-03" @default.
- W4312779997 title "Composite FORCE Learning of Chaotic Echo State Networks for Time-Series Prediction" @default.
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- W4312779997 doi "https://doi.org/10.23919/ccc55666.2022.9901897" @default.
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