Hybrid Support Vector Regression and Genetic Algorithm Technique – A Novel Approach in Process ModelingChemical Product and Process Modeling (2009)
AbstractThis paper describes a robust support vector regression (SVR) methodology, which can offer superior performance for important process engineering problems. The method incorporates hybrid support vector regression and genetic algorithm technique (SVR-GA) for efficient tuning of SVR meta parameters. The algorithm has been applied for prediction of critical velocity of solid liquid slurry flow. A comparison with selected correlations in the literature showed that the developed SVR correlation noticeably improved prediction of critical velocity over a wide range of operating conditions, physical properties, and pipe diameters.
- support vector regression,
- genetic algorithm,
- slurry critical velocity
Publication DateMarch 28, 2009
Citation Informationsandip k lahiri. "Hybrid Support Vector Regression and Genetic Algorithm Technique – A Novel Approach in Process Modeling" Chemical Product and Process Modeling Vol. 4 Iss. 1, Article-4 (2009)
Available at: http://works.bepress.com/sandip_lahiri/16/