Multiscale Bayesian rectification of data from linear steady-state and dynamic systems without accurate models

Industrial and Engineering Chemistry Research

Volumn 40, Issue 1, 2001, Pages 261-274

  Bakshi, Bhavik R ^a   Nounou, Mohamed N ^a   Goel, Prem K ^b   Shen, Xiaotong ^b  

^a Ohio State University   (United States)

^b OHIO STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; OPTIMIZATION; PROBABILITY DISTRIBUTIONS; RANDOM ERRORS; RANDOM PROCESSES; STATISTICAL TESTS;

LINEAR STEADY STATE SYSTEMS; MULTISCALE BAYESIAN RECTIFICATION; WAVELETS COEFFICIENTS;

MEASUREMENT ERRORS;

ACCURACY; ARTICLE; BAYES THEOREM; CALCULATION; CHEMICAL ENGINEERING; DYNAMICS; STATISTICAL ANALYSIS; STATISTICAL MODEL; SYSTEM ANALYSIS;

EID: 0035151098     PISSN: 08885885     EISSN: None     Source Type: Journal    
DOI: 10.1021/ie990905p     Document Type: Article

References (3)

1. Tham, M. T.; Parr, A. Succeed at On-Line Validation and Reconstruction of Data. Chem. Eng. Prog. 1994, 90, 46.
2. Kramer M. A.; Mah, R. S. H. Model-Based Monitoring. Proceedings od the International Conference on Foundations of Computer Aided Process Operations; Rippin, D., Hale, J., Davis, J., Eds.; CACHE: Austin, TX, 1994.
3. Karjala, T. W.; Himmelblau, D. M. Dynamic Data Rectification by Recurrent Neural Networks vs Traditional Methods. AIChE J. 1994, 40, 1865.