Discrete-time recurrent high order neural networks for nonlinear identification

DOI10.1016/j.jfranklin.2010.05.018zbMath1202.93082OpenAlexW2093580138WikidataQ59313984 ScholiaQ59313984MaRDI QIDQ609792

Alma Y. Alanis, Alexander G. Loukianov, Edgar N. Sanchez, Esteban A. Hernandez

Publication date: 1 December 2010

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jfranklin.2010.05.018

zbMATH Keywords

stability analysisLyapunov approachdiscrete-time nonlinear system identificationrecurrent high order neural networks

Mathematics Subject Classification ID

Neural networks for/in biological studies, artificial life and related topics (92B20) System identification (93B30) Nonlinear systems in control theory (93C10) Discrete-time control/observation systems (93C55) Robust stability (93D09)

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Cites Work

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• Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms.
• Neural networks for modelling and control of dynamic systems. A practitioner's handbook
• Adaptive mnn control for a class of non-affine narmax systems with disturbances

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