期刊
NEUROCOMPUTING
卷 329, 期 -, 页码 86-96出版社
ELSEVIER
DOI: 10.1016/j.neucom.2018.10.008
关键词
Identification; Neural networks; Dynamical systems; Lyapunov methods
资金
- Fundacao de Amparo do Distrito Federal - FAPDF [1099/2016]
- University of Brasilia [23106.007407/2016-51]
- Department of Electrical and Computer Engineering at the University of Alberta
This study is concerned with the asymptotic identification of nonlinear systems based on Lyapunov theory and two-layer neural networks. An improved identification model enhanced with a feedback term and a novel adaptation law for the threshold offset, associated with the output weight matrix, is introduced to assure the convergence of the online prediction error, even in the presence of approximation error and bounded disturbances and when upper bounds for these perturbations are not known in advance. The effectiveness of the proposed method and its application to the identification of a hyperchaotic system and control of a welding system is investigated. (C) 2018 Elsevier B.V. All rights reserved.
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