Open this publication in new window or tab >>2013 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 49, no 7, p. 2053-2063Article in journal (Refereed) Published
Abstract [en]
We present a novel method for Wiener system identification. The method relies on a semiparametric, i.e. a mixed parametric/nonparametric, model of a Wiener system. We use a state-space model for the linear dynamical system and a nonparametric Gaussian process model for the static nonlinearity. We avoid making strong assumptions, such as monotonicity, on the nonlinear mapping. Stochastic disturbances, entering both as measurement noise and as process noise, are handled in a systematic manner. The nonparametric nature of the Gaussian process allows us to handle a wide range of nonlinearities without making problem-specific parameterizations. We also consider sparsity-promoting priors, based on generalized hyperbolic distributions, to automatically infer the order of the underlying dynamical system. We derive an inference algorithm based on an efficient particle Markov chain Monte Carlo method, referred to as particle Gibbs with ancestor sampling. The method is profiled on two challenging identification problems with good results. Blind Wiener system identification is handled as a special case.
Place, publisher, year, edition, pages
Elsevier, 2013
Keywords
System identification, Wiener, Block-oriented models, Gaussian process, Semiparametric, Particle filter, Ancestor sampling, Particle Markov chain Monte Carlo
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-95954 (URN)10.1016/j.automatica.2013.03.021 (DOI)000321233900011 ()
Note
Funding Agencies|project Calibrating Nonlinear Dynamical Models|621-2010-5876|Swedish Research Council||CADICS||Linnaeus Center||
2013-08-192013-08-122017-12-06