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Journal ArticleDOI

Decoupling the linear and nonlinear parts in Hammerstein model identification

Er-Wei Bai
- 01 Apr 2004 - 
- Vol. 40, Iss: 4, pp 671-676
TLDR
In this article, the linear part of a Hammerstein model is decoupled from the nonlinear part in model identification, and the identification of the linear parts becomes a linear problem and accordingly enjoys the same convergence and consistency results as if the unknown nonlinearity is absent.
About: 
This article is published in Automatica.The article was published on 2004-04-01. It has received 112 citations till now. The article focuses on the topics: System identification & Nonlinear system.

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Citations
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Journal ArticleDOI

Identification of Hammerstein nonlinear ARMAX systems

Feng Ding, +1 more
- 01 Sep 2005 - 
TL;DR: Two identification algorithms are developed for Hammerstein nonlinear systems with memoryless nonlinear blocks and linear dynamical blocks described by ARMAX/CARMA models to replace unmeasurable noise terms in the information vectors by their estimates, and to compute the noise estimates based on the obtained parameter estimates.
Journal ArticleDOI

Model selection approaches for non-linear system identification: a review

Xia Hong, +5 more
- 01 Oct 2008 - 
TL;DR: A systematic overview of basic research on model selection approaches for linear-in-the-parameter models, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design is presented.
Journal ArticleDOI

Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems

Dongqing Wang, +1 more
- 01 Dec 2008 - 
TL;DR: An extended stochastic gradient algorithm is developed to estimate the parameters of Hammerstein-Wiener ARMAX models to improve the identification accuracy and results indicate that the parameter estimation errors become small by introducing the forgetting factor.
Journal ArticleDOI

A two-stage algorithm for identification of nonlinear dynamic systems

Kang Li, +2 more
- 01 Jul 2006 - 
TL;DR: This paper investigates the two-stage stepwise identification for a class of nonlinear dynamic systems that can be described by linear-in-the-parameters models, and the model has to be built from a very large pool of basis functions or model terms.
Journal ArticleDOI

Identification and Control for Heart Rate Regulation During Treadmill Exercise

Steven W. Su, +4 more
- 18 Jun 2007 - 
TL;DR: A novel integrated approach for the identification and control of Hammerstein systems to achieve desired heart rate profile tracking performance for an automated treadmill system and the proposed algorithm achieves much better heart rate tracking performance.
References
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Book

System Identification: Theory for the User

Lennart Ljung
TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
Book

System identification

Torsten Söderström, +1 more
Journal ArticleDOI

Optimal estimation theory for dynamic systems with set membership uncertainty: an overview

Mario Milanese, +1 more
- 01 Nov 1991 - 
TL;DR: In this article, the main results of this theory are reviewed, with special attention to the most recent advances obtained in the case of componentwise bounds, where the uncertainty is described by an additive noise which is known only to have given integral (typically l 1 or l 2) or componentwise (l ∞) bounds.
Journal ArticleDOI

An iterative method for the identification of nonlinear systems using a Hammerstein model

Kumpati S. Narendra, +1 more
- 01 Jul 1966 - 
TL;DR: In this article, an iterative method is proposed for the identification of nonlinear systems from samples of inputs and outputs in the presence of noise, which consists of a no-memory gain (of an assumed polynomial form) followed by a linear discrete system.
Book

Adaptive Control of Systems with Actuator and Sensor Nonlinearities

Gang Tao, +1 more
TL;DR: In this article, an in-depth examination of intelligent approaches to increasing the accuracy of a variety of system components is presented, utilizing a unified, adaptive, inverse approach, which offers electrical, mechanical, chemical, aeronautical and computer engineers methods for controlling many of the "hard" nonlinearities of frequently employed control systems such as dead-zone, backlash and hysteresis.