Document Type
Article
Publication Date
7-15-2000
Publication Title
Computers & Chemical Engineering
Abstract
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.
Repository Citation
Ungarala, Sridhar and Bakshi, Bhavik R., "A Multiscale, Bayesian and Error-In-Variables Approach for Linear Dynamic Data Rectification" (2000). Chemical & Biomedical Engineering Faculty Publications. 71.
https://engagedscholarship.csuohio.edu/encbe_facpub/71
Original Citation
Ungarala, S., , & Bakshi, B. (2000). A multiscale, Bayesian and error-in-variables approach for linear dynamic data rectification. Computers and Chemical Engineering, 24(2-7), 445-451. doi:10.1016/S0098-1354(00)00436-1
Volume
24
Issue
4-7
DOI
10.1016/S0098-1354(00)00436-1
Version
Postprint
Publisher's Statement
NOTICE: this is the author’s version of a work that was accepted for publication in Computers & Chemical Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Chemical Engineering, 24, 2-7, (July 15, 2000) DOI 10.1016/S0098-1354(00)00436-1
