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.
MATLAB code
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 identification. Automatica, 49(7): 2053-2063, July 2013. [pdf] [Automatica]