Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms.

DOI10.1016/j.ins.2003.08.002zbMath1049.93019OpenAlexW2165208076MaRDI QIDQ1429837

Wen Yu

Publication date: 27 May 2004

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2003.08.002

zbMATH Keywords

system identificationlearningdiscrete-time neural networksinput-to-state stability approach

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20) System identification (93B30) Discrete-time control/observation systems (93C55) Input-output approaches in control theory (93D25)

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Cites Work

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• Neurons with graded response have collective computational properties like those of two-state neurons.
• Approximation by superpositions of a sigmoidal function
• Input-to-state stability for discrete-time nonlinear systems

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