Development of inferential measurements using neural networks

ISA Transactions

Volumn 40, Issue 4, 2001, Pages 307-323

  Bhartiya, Sharad ^a   Whiteley, James R ^a  

^a Oklahoma State University   (United States)

Author keywords

ASTM product property; Inferential measurements; Neural networks; Subset selection

Indexed keywords

CONTROL SYSTEMS; CORRELATION METHODS; OPTIMIZATION; PETROLEUM PRODUCTS; PETROLEUM REFINERIES; QUALITY CONTROL; STANDARDS; STATISTICAL METHODS;

INFERENTIAL MEASUREMENT; NONLINEAR CORRELATIONS; ONLINE CONTROL; SUBSET SELECTION;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; INDUSTRY; REGRESSION ANALYSIS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; INDUSTRY; NEURAL NETWORKS (COMPUTER); REGRESSION ANALYSIS;

EID: 0034741626     PISSN: 00190578     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0019-0578(01)00004-0     Document Type: Article

References (22)

1. Development of inferential process models using PLS
2. Self-validating inferential sensors with application to air emission monitoring
3. Characterizations of an empirical influence function for detecting influential cases in regression
4. A note on the variance of a predicted response in regression
5. Mean square eror of prediction as a criterion for selecting variables
6. Developments in linear regression methodology: 1959-1982
7. p
8. Selection of the best subset in regression analysis
9. A new look at the statistical model identification
10. Estimating the dimension of a model
11. Progress in supervised neural networks - What's new since Lippmann
12. Gauss-Newton approximation to Bayesian regularization
13. Inferential control - Part 1; crude unit advanced controls pass accuracy and repeatability tests
14. Inferential control - Conclusion; crude unit controls reduce quality giveaway, increase profits