Revisiting Hammerstein system identification through the two-stage algorithm for bilinear parameter estimation

DOI10.1016/j.automatica.2009.07.033zbMath1180.93031OpenAlexW1994984923WikidataQ59591876 ScholiaQ59591876MaRDI QIDQ1049161

Lennart Ljung, Qing-Hua Zhang, Jian-Dong Wang

Publication date: 8 January 2010

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-97738

zbMATH Keywords

Hammerstein systemnonlinear system identificationbilinear equationtwo-stage algorithm

Mathematics Subject Classification ID

System identification (93B30) Nonlinear systems in control theory (93C10)

Related Items (11)

Hierarchical stochastic gradient algorithm and its performance analysis for a class of bilinear-in-parameter systems ⋮ Finite model order accuracy in Hammerstein model estimation ⋮ The gradient-based iterative estimation algorithms for bilinear systems with autoregressive noise ⋮ Making parametric Hammerstein system identification a linear problem ⋮ A bias-corrected estimator for nonlinear systems with output-error type model structures ⋮ Subspace identification methods for Hammerstein systems: rank constraint and dimension problem ⋮ Identification of extended Hammerstein systems with hysteresis-type input nonlinearities described by Preisach model ⋮ The filtering based parameter identification for bilinear-in-parameter systems ⋮ Modeling and identification of uncertain-input systems ⋮ Iterative parameter estimation for signal models based on measured data ⋮ Adaptive control and signal processing literature survey (No. 16)

Cites Work

• Unnamed Item
• Unnamed Item
• Identification of nonlinear systems using neural networks and polynomial models. A block-oriented approach.
• On covariance function tests used in system identification
• An optimal two-stage identification algorithm for Hammerstein-Wiener nonlinear systems
• An estimator of the inverse covariance matrix and its application to ML parameter estimation in dynamical systems
• Identification of MIMO Hammerstein models using least squares support vector machines
• Least squares solutions of bilinear equations
• Subspace-based identification algorithms for Hammerstein and Wiener models
• Identification of Hammerstein systems without explicit parameterisation of non-linearity

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