Bayesian identification of Wiener systems

This code implements our method for Bayesian 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.


This code is written by Fredrik Lindsten and it is available here, see also Fredrik’s software page

Relevant paper

Fredrik Lindsten, Thomas B. Schön and Michael I. Jordan. Bayesian semiparametric Wiener system identificationAutomatica, 49(7): 2053-2063, July 2013. [pdf] [Automatica]

 © Thomas Schön 2022