On realizability of neural networks-based input--output models in the classical state-space form

DOI10.1016/j.automatica.2006.03.003zbMath1117.93368OpenAlexW2062931881MaRDI QIDQ856548

Sven Nõmm, Fahmida N. Chowdhury, Yu. R. Kotta

Publication date: 7 December 2006

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2006.03.003

zbMATH Keywords

neural networksrealizabilitydiscrete-time nonlinear systemsinput--output models

Mathematics Subject Classification ID

Nonlinear systems in control theory (93C10) Stochastic systems in control theory (general) (93E03)

Related Items (2)

Input-output linearization of discrete-time systems by dynamic output feedback ⋮ Unnamed Item

Uses Software

• NNSYSID

Cites Work

• State observability in recurrent neural networks
• Two Approaches for State Space Realization of NARMA Models: Bridging the Gap
• Linearization of Discrete-Time Systems
• Predictive control based on neural network models with I/O feedback linearization
• Forward accessibility for recurrent neural networks
• Transfer equivalence and realization of nonlinear higher order input-output difference equations

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