Markov chain approach to identifying Wiener systems

DOI10.1007/s11432-012-4582-yzbMath1245.93134OpenAlexW1965055646MaRDI QIDQ439801

Chen, Hanfu, Wen-Xiao Zhao

Publication date: 17 August 2012

Published in: Science China. Information Sciences (Search for Journal in Brave)

Full work available at URL: http://engine.scichina.com/doi/10.1007/s11432-012-4582-y

zbMATH Keywords

recursive identificationstochastic approximationMarkov chainstrong consistencyWiener system

Mathematics Subject Classification ID

Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Identification in stochastic control theory (93E12)

Related Items (8)

Recursive identification of errors-in-variables Wiener systems ⋮ Identification of errors-in-variables systems with general nonlinear output observations and with ARMA observation noises ⋮ Identification of Wiener, Hammerstein, and NARX systems as Markov chains with improved estimates for their nonlinearities ⋮ Estimation of IIR systems with binary-valued observations ⋮ On \(1/f\) noise ⋮ Hankel matrices for system identification ⋮ Recursive identification of systems with binary-valued outputs and with ARMA noises ⋮ Recursive identification of quantized linear systems

Cites Work

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• Identification for Wiener systems with internal noise
• The identification of nonlinear biological systems: Wiener and Hammerstein Cascade models
• Strong consistency and rates for recursive probability density estimators of stationary processes
• Identification and stochastic adaptive control
• Frequency domain identification of Wiener models
• Stochastic approximation and its applications
• Nonlinear time series. Nonparametric and parametric methods
• Maximum likelihood identification of Wiener models
• Identification of errors-in-variables systems with ARMA observation noises
• Identification for Wiener systems with RTF subsystems
• Strong consistence of recursive identification for Wiener systems
• General Irreducible Markov Chains and Non-Negative Operators
• Fading memory and the problem of approximating nonlinear operators with Volterra series
• Mixing Conditions for Markov Chains
• Convergence analysis of recursive identification algorithms based on the nonlinear Wiener model
• Identifying MIMO Hammerstein systems in the context of subspace model identification methods
• Recursive Identification for Nonlinear ARX Systems Based on Stochastic Approximation Algorithm
• Recursive Identification for Wiener Model With Discontinuous Piece-Wise Linear Function
• Parameter identification of Wiener systems with discontinuous nonlinearities

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