Identification of Hammerstein-Wiener models - DiVA portal.pdfVIP

Identification of Hammerstein-Wiener models Adrian Wills, Thomas Schön, Lennart Ljung and Brett Ninness Linköping University Post Print N.B.: When citing this work, cite the original article. Original Publication: Adrian Wills, Thomas Schön, Lennart Ljung and Brett Ninness, Identification of Hammerstein-Wiener models, 2013, Automatica, (49), 1, 70-81. /10.1016/j.automatica.2012.09.018 Copyright: Elsevier / Postprint available at: Linköping University Electronic Press http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-89528 Identification of Hammerstein–Wiener Models ? Adrian Wills a , Thomas B. Sch¨on b , Lennart Ljung b , Brett Ninness a a School of Electrical Engineering and Computer Science, University of Newcastle, Callaghan, NSW 2308, Australia b Division of Automatic Control, Link¨oping University, SE-581 83 Link¨oping, Sweden Abstract This paper develops and illustrates a new maximum-likelihood based method for the identification of Hammerstein–Wiener model structures. A central aspect is that a very general situation is considered wherein multivariable data, non-invertible Hammerstein and Wiener nonlinearities, and coloured stochastic disturbances both before and after the Wiener nonlinearity are all catered for. The method developed here addresses the blind Wiener estimation problem as a special case. Key words: System identification, Hammerstein, Wiener, block-oriented models, nonlinear models, dynamic systems, Monte Carlo method, smoothing, expectation maximisation algorithm, particle methods, Maximum Likelihood. 1 Introduction ⌫t µt