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Full-Text Articles in Chemical Engineering
Time-Varying System Identification Using Modulating Functions And Spline Models With Application To Bio-Processes, Sridhar Ungarala, Tomas B. Co
Time-Varying System Identification Using Modulating Functions And Spline Models With Application To Bio-Processes, Sridhar Ungarala, Tomas B. Co
Chemical & Biomedical Engineering Faculty Publications
Time dependent parameters are frequently encountered in many real processes which need to be monitored for process modeling, control and supervision purposes. Modulating functions methods are especially suitable for this task because they use the original continuous-time differential equations and avoid differentiation of noisy signals. Among the many versions of the method available, Pearson–Lee method offers a computationally efficient alternative. In this paper, Pearson–Lee method is generalized for non-stationary continuous-time systems and the on-line version is developed. The time dependent parameters are modeled as polynomial splines inside a moving data window and recursion formulae using shifting properties of …
A Multiscale, Bayesian And Error-In-Variables Approach For Linear Dynamic Data Rectification, Sridhar Ungarala, Bhavik R. Bakshi
A Multiscale, Bayesian And Error-In-Variables Approach For Linear Dynamic Data Rectification, Sridhar Ungarala, Bhavik R. Bakshi
Chemical & Biomedical Engineering Faculty Publications
A multiscale approach to data rectification is proposed for data containing features with different time and frequency localization. Noisy data are decomposed into contributions at multiple scales and a Bayesian optimization problem is solved to rectify the wavelet coefficients at each scale. A linear dynamic model is used to constrain the optimization problem, which facilitates an error-in variables (EIV) formulation and reconciles all measured variables. Time-scale recursive algorithms are obtained by propagating the prior with temporal and scale models. The multi-scale Kalman filter is a special case of the proposed Bayesian EIV approach.