- Tákacs G. (2009) Electrical submersible pumps manual: design, operations, and maintenance. Gulf Professional Publishing. [Google Scholar]
- Varon M.P. (2013) Estudo de uma bomba centrífuga submersa (BCS) como medidor de vazão, Msc Thesis, FEM-UNICAMP, Campinas, Brazil. [Google Scholar]
- Snyder J.R., Dale R., Joe Haws H. (1989) Pump off/gas lock motor controller for electrical submersible pumps, EP Patent App. EP19,880,202,383, 03 May. [Google Scholar]
- Klein F.L., Seleghim, Jr. P., Eric H. (2004) Time-frequency analysis of intermittent two-phase flows in horizontal piping, J. Braz. Soc. Mech. Sci. Eng. 26, 2, 174–179. [CrossRef] [Google Scholar]
- Kolpak M.K., Rock T.J. (1996) Measuring vibration of a fluid stream to determine gas fraction, U.S. Patent No. 5,524,475, 11 June. [Google Scholar]
- Henry M.P., Richard R.P. (2014) Multiphase flow metering system, U.S. Patent Application No 14/135,085, 19 Dec. [Google Scholar]
- Nelles O. (2001) Nonlinear system identification, Springer-Verlag, Berlin Heidelberg. [CrossRef] [Google Scholar]
- Ljung L. (1999) System identification: Theory for the user, Prentice Hall PTR, Upper Saddle River, NJ. [Google Scholar]
- Dietterich T.G. (1986) Learning at the knowledge level, Mach. Lear. 1, 287–315. [Google Scholar]
- Rychetsky M. (2001) Algorithms and architectures for machine learning based on regularized neural networks and support vector approaches, Shaker Verlag GmbH, Germany. [Google Scholar]
- Vapnik V. (1995) The nature of statistical learning theory, Springer-Verlag, New York. [CrossRef] [Google Scholar]
- Vapnik V., Golowich S., Steven E., Smola A. (1996) Support vector method for function approximation, regression estimation, and signal processing, Advances in Neural Information Processing Systems 9, pp. 281–287. [Google Scholar]
- Stoean R., Dumitrescu D., Preuss M., Stoean C. (2006) Evolutionary support vector regression machines, in: SYNASC '06 Proceedings of the Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, IEEE Computer Society, Washington, DC, pp. 330–335. [Google Scholar]
- Zhang H., Yongmei L. (2013) BSP-Based Support Vector Regression Machine Parallel Framework, Computer and Information Science (ICIS), 2013 IEEE/ACIS 12th International Conference on. IEEE, Niigata, Japan, pp. 329–334. [Google Scholar]
- Yu Q., Liu Y., Rao F. (2009) Parameter selection of support vector regression machine based on differential evolution algorithm. Fuzzy systems and knowledge discovery, de FSKD ’09. Sixth International Conference, pp. 596–598. [Google Scholar]
- Zhenyue H., Mei C. (2009) Soft sensor modeling using SVR based on genetic algorithm and akaike information criterion, intelligent human-machine systems and cybernetics, Intelligent Human-Machine Systems and Cybernetics, 2009. IHMSC’09. International Conference on, Vol. 2, IEEE. [Google Scholar]
- Blackman R.B., Tukey J.W. (1959) The measurement of power spectra, Dover publications, New York. [Google Scholar]
- Michael K. (1996) A bound on the error of cross validation using the approximation and estimation rates, with consequences for the training-test split, Advances in Neural Information Processing Systems, pp. 183–189. [Google Scholar]
Open Access
Numéro |
Oil & Gas Science and Technology - Rev. IFP Energies nouvelles
Volume 73, 2018
|
|
---|---|---|
Numéro d'article | 29 | |
Nombre de pages | 7 | |
DOI | https://doi.org/10.2516/ogst/2018031 | |
Publié en ligne | 3 septembre 2018 |
Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.
Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.
Le chargement des statistiques peut être long.