DocumentCode
419046
Title
Symbolic regression modeling of blown film process effects
Author
Kordon, Arthur K. ; Lue, XChing-Tai
Author_Institution
Univation Technol., LLC, Baytown, TX, USA
Volume
1
fYear
2004
fDate
19-23 June 2004
Firstpage
561
Abstract
The potential of symbolic regression for automatic generation of process effects empirical models has been explored on a real industrial case study. A methodology based on nonlinear variable selection and model derivation by genetic programming has been defined and successfully applied for blown film process effects modeling. The derived nonlinear models are simple, have better performance than the linear models, and predicted behavior in accordance with the process physics.
Keywords
chemical technology; computational complexity; genetic algorithms; regression analysis; automatic process generation; blown film process effects; genetic programming; industrial case study; model derivation; nonlinear variable selection; process effects empirical models; process physics; symbolic regression; Chemical industry; Chemical processes; Chemical technology; Genetic programming; Input variables; Neural networks; Predictive models; Product development; Robustness; US Department of Energy;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2004. CEC2004. Congress on
Print_ISBN
0-7803-8515-2
Type
conf
DOI
10.1109/CEC.2004.1330907
Filename
1330907
Link To Document