Predictive neuro-control of uncertain systems: Design and use of a neuro-optimizer
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Publication:1400310
DOI10.1016/S0005-1098(03)00005-0zbMath1032.93019OpenAlexW2029675693MaRDI QIDQ1400310
Jean-Pierre Vila, Vérène Wagner
Publication date: 13 August 2003
Published in: Automatica (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/s0005-1098(03)00005-0
Predictive controlstabilizationNeural networkstochastic stabilityUncertain systemsanaerobic digestionBayesian selectionuncertain time-varying system
Neural networks for/in biological studies, artificial life and related topics (92B20) Design techniques (robust design, computer-aided design, etc.) (93B51) Optimal stochastic control (93E20) Stochastic stability in control theory (93E15)
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Cites Work
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- Optimal infinite-horizon feedback laws for a general class of constrained discrete-time systems: Stability and moving-horizon approximations
- Neural networks for control systems - a survey
- A receding-horizon regulator for nonlinear systems and a neural approximation
- Bayesian learning for neural networks
- Neural networks for modelling and control of dynamic systems. A practitioner's handbook
- A stable one-step-ahead predictive control of nonlinear systems
- Constrained model predictive control: Stability and optimality
- Receding horizon control of nonlinear systems
- Nonlinear stabilization by receding-horizon neural regulators
- Universal approximation bounds for superpositions of a sigmoidal function
- Nonparametric Estimation and Adaptive Control of Functional Autoregressive Models
- Adaptive control of a class of nonlinear discrete-time systems using neural networks