Shima Khatibisepehr

A Variational Bayesian Approach to Robust Identification of Switched ARX Models

A variational Bayesian approach to robust identification of switched auto-regressive exogenous models is developed in this paper. By formulating the problem of interest under a full Bayesian identification framework, the number of local-models can be determined automatically, while accounting for the uncertainty of parameter estimates in the overall identification procedure. A set of significance coefficients is used to assign proper importance weights to local-models. By maximizing the marginal likelihood of the identification data, insignificant local-models will be suppressed and the optimal number of local-models can be determined. Considering the fact that the identification data may be contaminated with outliers, t distributions with adjustable tails are utilized to model the contaminating noise so that the proposed identification algorithm is robust. The effectiveness of the proposed Bayesian approach is demonstrated through a simulated example as well as a detailed industrial application.

Research article: Data-based modeling and prediction of cytotoxicity induced by contaminants in water resources

This paper is concerned with dynamic modeling, prediction and analysis of cell cytotoxicity induced by water contaminants. A real-time cell electronic sensing (RT-CES) system has been used for continuously monitoring dynamic cytotoxicity responses of living cells. Cells are grown onto the surfaces of the microelectronic sensors. Changes in cell number expressed as cell index (CI) have been recorded on-line as time series. The CI data are used to develop dynamic prediction models for cell cytotoxicity process. We consider support vector regression (SVR) algorithm to implement data-based system identification for dynamic modeling and prediction of cytotoxicity. Through several validation studies, multi-step-ahead predictions are calculated and compared with the actual CI obtained from experiments. It is shown that SVR-based dynamic modeling has great potential in predicting the cytotoxicity response of the cells in the presence of toxicant.

A variational Bayesian approach to identification of switched ARX models

In the identification of switched Auto-Regressive eXogenous (SARX) models, the number of local models is often assumed to be known a priori. However, in many industrial applications the prior process knowledge or the available information about the plant operation might not be sufficient to determine the number of local models. In such cases, the optimal number of local models needs to be inferred from collected operational data. The switching mechanism of the process is also often unknown. Therefore, classical SARX identification methods assuming a piecewise affine system fail to accurately identify randomly switched models. Furthermore, classical identification methods result in single-point estimates of unknown parameters, thereby ignoring the parameter uncertainty. The main objective of this work is to formulate and solve the problem of SARX model identification under the variational Bayesian framework through which the aforementioned challenging issues can be addressed. As a full Bayesian system identification approach, the proposed method not only provides a posterior distribution over model parameters to reveal the level of uncertainty of the estimated values, but also determines the optimal number of local models automatically. Since the identification pair identity at each sampling instant can be inferred from the data set, the switching mechanism will not influence the identification results. The effectiveness of the proposed Bayesian approach is demonstrated through a simulation case study.

Real-Time Performance Assessment of Inferential Sensors

Abstract A data-driven Bayesian framework for real-time performance assessment of inferential sensors is proposed. The application of the proposed Bayesian solution does not depend on the identification techniques employed for inferential model development. The effectiveness of the proposed method is demonstrated through a simulation case study.