Multilayer dynamic neural networks for non-linear system on-line identification

DOI10.1080/00207170110089816zbMath1016.93019OpenAlexW2166302960MaRDI QIDQ3151516

Xiaoou Li, Alexander S. Poznyak, Wen Yu

Publication date: 16 October 2002

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

Full work available at URL: https://doi.org/10.1080/00207170110089816

zbMATH Keywords

system identificationerror boundmultilayer dynamic neural networksstable learning law

Mathematics Subject Classification ID

Related Items (8)

\(H_{\infty}\) tracking-based adaptive fuzzy-neural control for MIMO uncertain robotic systems with time delays ⋮ ELM with guaranteed performance for online approximation of dynamical systems ⋮ Global convergence of Oja's PCA learning algorithm with a non-zero-approaching adaptive learning rate ⋮ Convergence analysis of deterministic discrete time system of a unified self-stabilizing algorithm for PCA and MCA ⋮ Dr. Alexander Semionovich Poznyak Gorbatch: Biography ⋮ Nonlinear system identification using discrete-time recurrent neural networks with stable learning algorithms. ⋮ A nonlinear robust PI controller for an uncertain system ⋮ Dynamic system identification via recurrent multilayer perceptrons

Cites Work

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• Neural networks for control systems - a survey
• Neurons with graded response have collective computational properties like those of two-state neurons.
• Adaptive control of unknown plants using dynamical neural networks

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