Nonlinear system identification using neural state space models, applicable to robust control design

DOI10.1080/00207179508921536zbMath0825.93943OpenAlexW2004175622MaRDI QIDQ4844520

Bart De Moor, Joos Vandewalle, Johan A. K. Suykens

Publication date: 28 November 1995

Published in: International Journal of Control (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00207179508921536

Mathematics Subject Classification ID

Sensitivity (robustness) (93B35) Neural networks for/in biological studies, artificial life and related topics (92B20) Nonlinear systems in control theory (93C10) Identification in stochastic control theory (93E12)

Related Items

Standard representation and unified stability analysis for dynamic artificial neural network models, Identification of block-oriented nonlinear systems starting from linear approximations: a survey, Deep subspace encoders for nonlinear system identification, Efficient hinging hyperplanes neural network and its application in nonlinear system identification, Identification of nonlinear systems using polynomial nonlinear state space models, Unnamed Item, Application of NL\(_q\) neural control theory to a ball and beam system, Computationally efficient identification of continuous-time Lur'e-type systems with stability guarantees, Combining neural and conventional paradigms for modelling,prediction and control, Dynamic state feedback controller and observer design for dynamic artificial neural network models

Uses Software

• Matlab

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Neural networks for control systems - a survey
• 4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems
• Multilayer feedforward networks are universal approximators
• Controller design for plants with structured uncertainty
• The complex structured singular value
• Non-linear system identification using neural networks
• Properties of neural networks with applications to modelling non-linear dynamical systems
• Model validation: a connection between robust control and identification
• Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems
• Learning representations by back-propagating errors
• Approximation by superpositions of a sigmoidal function