Identifying chaotic systems using Wiener and Hammerstein cascade models

DOI10.1016/S0895-7177(00)00256-9zbMath0992.37074WikidataQ126351098 ScholiaQ126351098MaRDI QIDQ5936760

Guan-Rong Chen, Ming Xu, Yan-Tao Tian

Publication date: 8 July 2001

Published in: Mathematical and Computer Modelling (Search for Journal in Brave)

zbMATH Keywords

artificial neural networkscascade modelsHammerstein modelWiener model

Mathematics Subject Classification ID

Strange attractors, chaotic dynamics of systems with hyperbolic behavior (37D45) Time series analysis of dynamical systems (37M10)

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Identification of chaotic system using Hammerstein-ELM model ⋮ Auxiliary model based recursive generalized least squares parameter estimation for Hammerstein OEAR systems ⋮ \(n\)-dimensional chaotic map with application in secure communication ⋮ Maximum likelihood stochastic gradient estimation for Hammerstein systems with colored noise based on the key term separation technique ⋮ A simple method to synchronize chaotic systems and its application to secure communications ⋮ Multi-step ahead nonlinear identification of Lorenz's chaotic system using radial basis neural network with learning by clustering and particle swarm optimization ⋮ Modeling nonlinear dynamics and chaos: a review

Cites Work

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• The identification of nonlinear biological systems: Wiener and Hammerstein Cascade models
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• Nonparametric identification of Wiener systems by orthogonal series
• SYSTEM IDENTIFICATION AND MODEL-BASED CONTROL OF A CHAOTIC SYSTEM
• FEEDBACK CONTROL OF LYAPUNOV EXPONENTS FOR DISCRETE-TIME DYNAMICAL SYSTEMS
• ON FEEDBACK CONTROL OF CHAOTIC NONLINEAR DYNAMIC SYSTEMS
• FROM CHAOS TO ORDER — PERSPECTIVES AND METHODOLOGIES IN CONTROLLING CHAOTIC NONLINEAR DYNAMICAL SYSTEMS
• Taming chaos. I. Synchronization

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