Robust Estimation of Wiener Models in the Presence of Outliers Using the VB Approach

This article is concerned with the identification of a Wiener model, which is a widely used block-oriented nonlinear model. The identification of the Wiener model in the presence of outliers and process noises has not been well studied to date. We propose a graphical model to describe the Wiener process, where the outliers are modeled by Student's
$t\text{-}$
distribution. Due to the negative effects of the nonlinearity and latent variables on parameter estimation, the model is estimated using a variational Bayesian approach. We propose an effective method to approximate the posterior distributions of the latent variables. As a result, a new method for Wiener model identification is proposed. Compared with the straightforward maximum-likelihood estimation and the classical prediction error method, the proposed method is more robust and provides more accurate parameter estimates. The proposed method is used to identify the temperature dynamic of the phase-change cooling module of the high-performance chip.