Blind Maximum-likelihood Identification of Wiener and Hammerstein Nonlinear Block Structures

Despite their structural simplicity, Wiener and Hammerstein nonlinear model structures have been effective in many application areas, where linear modelling has failed, e.g., the chemical process industry [5, 13], microwave and radio frequency (RF) technology [4, 7, 19], seismology [21], biology [8], physiology and psychophysics [14]. They can also be used in model predictive control [28, 29].

[1]  Stephen A. Billings,et al.  Identification of systems containing linear dynamic and static nonlinear elements , 1982, Autom..

[2]  Wlodzimierz Greblicki,et al.  Nonlinearity estimation in Hammerstein systems based on ordered observations , 1996, IEEE Trans. Signal Process..

[3]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[4]  Duane Q Nykamp,et al.  Full identification of a linear-nonlinear system via cross-correlation analysis. , 2002, Journal of vision.

[5]  C. J. Clark,et al.  Time-domain envelope measurement technique with application to wideband power amplifier modeling , 1998 .

[6]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[7]  Karim Abed-Meraim,et al.  Blind system identification , 1997, Proc. IEEE.

[8]  Rik Pintelon,et al.  Blind maximum likelihood identification of Hammerstein systems , 2008, Autom..

[9]  Christian Jutten,et al.  Quasi-nonparametric blind inversion of Wiener systems , 2001, IEEE Trans. Signal Process..

[10]  Yves Rolain,et al.  Identification of invariants of (over)parameterized models: finite sample results , 1999, IEEE Trans. Autom. Control..

[11]  Rik Pintelon,et al.  Blind Maximum-Likelihood Identification of Wiener Systems , 2009, IEEE Transactions on Signal Processing.

[12]  Er-Wei Bai.  A blind approach to the Hammerstein-Wiener model identification , 2002, Autom..

[13]  José A. Romagnoli,et al.  Application of Wiener model predictive control (WMPC) to a pH neutralization experiment , 1999, IEEE Trans. Control. Syst. Technol..

[14]  Dimitrios Hatzinakos,et al.  Blind identification of LTI-ZMNL-LTI nonlinear channel models , 1995, IEEE Trans. Signal Process..

[15]  Dimitrios Hatzinakos,et al.  Blind identification of linear subsystems of LTI-ZMNL-LTI models with cyclostationary inputs , 1997, IEEE Trans. Signal Process..

[16]  Wei Wang,et al.  Generalized Predictive Control of Nonlinear Systems of the Hammerstein Form , 1994 .

[17]  J. Schoukens,et al.  Box-Jenkins identification revisited - Part I: Theory , 2006, Autom..

[18]  Fredrik Tjärnström,et al.  Computing Uncertainty Regions with Simultaneous Confidence Degree using Bootstrap , 2000 .

[19]  L. Tong,et al.  Multichannel blind identification: from subspace to maximum likelihood methods , 1998, Proc. IEEE.

[20]  M. J. Korenberg,et al.  The identification of nonlinear biological systems: Wiener and Hammerstein cascade models , 1986, Biological Cybernetics.

[21]  Stanley H. Johnson,et al.  Use of Hammerstein Models in Identification of Nonlinear Systems , 1991 .

[22]  B. Peeters,et al.  Stochastic System Identification for Operational Modal Analysis: A Review , 2001 .

[23]  R. Pintelon,et al.  Blind Maximum Likelihood Identification of Wiener Systems with Measurement Noise , 2009 .

[24]  Yves Rolain,et al.  Fast approximate identification of nonlinear systems , 2003, Autom..

[25]  Yucai Zhu.  Identification of Hammerstein models for control using ASYM , 2000 .

[26]  R. Fletcher.  Practical Methods of Optimization , 1988 .

[27]  Nicholas Kalouptsidis,et al.  Blind identification of Volterra-Hammerstein systems , 2005, IEEE Transactions on Signal Processing.