Robust extreme learning machine neural approach for uncertain nonlinear hyper‐chaotic system identification

DOI10.1002/RNC.5756zbMath1527.93036MaRDI QIDQ6082361

Ho Pham Huy Anh, Cao Van Kien

Publication date: 29 November 2023

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

zbMATH Keywords

Lyapunov stability principleadvanced single hidden layer neural extreme learning machine (ASHLN-ELM) modelextreme learning machine (ELM) algorithmonline adaptive identification ruleuncertain nonlinear hyper-chaotic system identification

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Sensitivity (robustness) (93B35) System identification (93B30) Nonlinear systems in control theory (93C10) Adaptive control/observation systems (93C40) Lyapunov and other classical stabilities (Lagrange, Poisson, (L^p, l^p), etc.) in control theory (93D05)

Related Items (1)

Proximal policy optimization‐based controller for chaotic systems

Cites Work

• Dynamic analysis and control of a new hyperchaotic finance system
• Multilayer feedforward networks are universal approximators
• Dynamics and synchronization of new hyperchaotic complex Lorenz system
• BRIDGE THE GAP BETWEEN THE LORENZ SYSTEM AND THE CHEN SYSTEM
• Approximation by superpositions of a sigmoidal function
• Unnamed Item

This page was built for publication: Robust extreme learning machine neural approach for uncertain nonlinear hyper‐chaotic system identification