Journal
NEUROCOMPUTING
Volume 275, Issue -, Pages 1511-1521Publisher
ELSEVIER
DOI: 10.1016/j.neucom.2017.09.092
Keywords
Sinusoidal echo state network; Periodic nonlinear systems; Discrete-time dynamic systems; System identification
Categories
Funding
- National Natural Science Foundation of China [61473070, 61433004, 61627809]
- Fundamental Research Funds for the Central Universities [N160406002]
- SAPI Fundamental Research Funds [2013ZCX01]
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In this paper, an improved identification method based on sinusoidal echo state network (SESN) is proposed to identify a class of periodic discrete-time dynamic nonlinear systems with or without noise. For periodic nonlinear systems, the sinusoidal state activation functions can provide more efficient mapping than the sigmoidal state activation functions. A matrix trace based online learning algorithm is constructed to train the output weights of SESN. Based on the Lyapunov stability theory, the asymptotical convergence of the identification error to zero is proved. A nonlinear autoregressive with exogenous (NARX) input model is used to validate the effectiveness of the proposed identification method. (C) 2017 Elsevier B.V. All rights reserved.
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