Identification of Nonlinear Systems Structured by Hammerstein-Wiener Model
Standard Hammerstein-Wiener models consist of a linear subsystem sandwiched by two memoryless nonlinearities. The problem of identifying Hammerstein-Wiener systems is addressed in the presence of linear subsystem of structure totally unknown and polynomial input and output nonlinearities. Presently, the system nonlinearities are allowed to be noninvertible. The system identification problem is dealt by developing a two-stage frequency identification method. First, the parameters of system nonlinearities are identified. In the second stage, a frequency approach is designed to estimate the linear subsystem frequency gain. All involved estimators are proved to be consistent.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1092385Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2095
 F. Taringou, O. Hammi, B. Srinivasan, R. Malhame, and F.M. Ghannouchi, "Behaviour modelling of wideband RF transmitters using Hammerstein-Wiener models,” IET Circuits Devices & Systems, vol. 4, no. 4, pp. 282-290, 2010.
 R. Abrahamsson, S.M. Kay, and P. Stoica, "Estimation of the parameters of a bilinear model with applications to submarine detection and system identification,” Digital Signal Processing, vol. 17, no. 4, pp. 756-773, Jul. 2007.
 H.J. Palanthandalam-Madapusi, A.J. Ridley, and D.S. Bernstein, "Identification and prediction of ionospheric dynamics using a Hammerstein-Wiener model with radial basis functions,” in Proc. American Control Conference, 2005, pp. 5052-5057.
 E.W. Bai and F. Giri, Block-Oriented Nonlinear System Identification. U.K: Springer, 1 edition, 2010.
 E. W. Bai, "A Blind Approach to the Hammerstein-Wiener Model Identification,” Automatica, vol. 38, no. 6, pp. 967-979, 2002.
 P. Crama and J. Schoukens, "Hammerstein-Wiener system estimator initialization,” Automatica, vol. 40, no. 9, pp. 1543-1550, 2004.
 B. Ni, M. Gilson, and H. Garnier, "Refined instrumental variable method for Hammerstein-Wiener continuous-time model identification,” IET Control Theory and Applications, vol. 7, no. 9, pp. 1276-1286, 2013.
 J. Vörös, "An iterative method for Hammerstein-Wiener systems parameter identification,” Journal of Electrical Engineering, vol. 55, no. 11-12, pp. 328-331, 2004.
 M. Schoukens, E.W. Bai, and Y. Rolain, "Identification of Hammerstein-Wiener Systems,” in Proc. 16th IFAC Symp. Syst. Identification, Brussels, 2012, pp. 274-279.
 D. Wang and F. Ding, "Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems,” Computers & Mathematics with Applications, vol. 56, no. 12, pp. 3157-3164, 2008.
 E.W. Bai, "An optimal two-stage identification algorithm for Hammerstein–Wiener Nonlinear Systems,” In Giri and Bai, Block-oriented Nonlinear System Identification. U.K: Springer, 2010, pp. 27-34.
 D. Kozen and S. Landau, "Polynomial decomposition algorithms,” J. Symbolic Computation, vol. 7, 445-456, 1989.
 G. Tao and P. Kokotovic, Adaptive Control of Systems with Actuator and Sensor Nonlinearities. US: John Wiley And Sons Ltd, 1996.
 Y.T.C. Yi Su and Y. Stepanenko, "Adaptive Control of a class of nonlinear Systems preceded by an unknown backlash-like hysteresis,” In Conf. 2000 IEEE Int. Conference on Decision and Control, Australia, pp. 1459-1464.