Comparison of extreme learning machine with support vector regression for reservoir permeability prediction

CIS 2009 - 2009 International Conference on Computational Intelligence and Security

Volumn 2, Issue , 2009, Pages 173-176

  Cheng, Guo Jian ^a   Cai, Lei ^a   Pan, Hua Xian ^a  

^a XI'AN SHIYOU UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVATION FUNCTIONS; EFFECTIVE LEARNING; EXTREME LEARNING MACHINE; FAST LEARNING; GENERALIZATION PERFORMANCE; HIDDEN LAYERS; HIDDEN NEURONS; INPUT WEIGHTS; LEARNING SPEED; LOCAL MINIMUMS; OPTIMAL SOLUTIONS; PARAMETER SELECTION; RESERVOIR PERMEABILITY; SINGLE-HIDDEN LAYER FEED-FORWARD NEURAL NETWORK; SUPPORT VECTOR REGRESSIONS; USER-DEFINED PARAMETERS;

CAPILLARITY; FORECASTING; LEARNING SYSTEMS; MECHANICAL PERMEABILITY; NEURAL NETWORKS; PETROLEUM RESERVOIR ENGINEERING;

LEARNING ALGORITHMS;

EID: 77949290310     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CIS.2009.124     Document Type: Conference Paper

References (18)

1. Osman, EA, Abdel-Wahhab, O.A. and Al-Marhoun, M.A.: "Prediction of Oil PVT Properties Using Neural Networks," paper SPE 68233 presented at the 2001 SPE Middle East Oil Show and Conference, Bahrain, March 17-20.
2. Wong, K.W., Ong, Y.S., Gedeon, T.D.etc. Reservoir Characterization Using Support Vector Machines.Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on Volume 2, Issue , 28-30 Nov. 2005 Page(s):354 - 359.
3. Huang, G.B., Zhu, Q.Y. & Siew, C.K.: Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks. International Joint Conference on Neural Networks (IJCNN'2004), (2004).E. H. Miller, "A note on reflector arrays (Periodical style-Accepted for publication)," IEEE Trans. Antennas Propagat., to be published.
4. Huang, G.-B. Zhu, Q.-Y. & Siew, C.-K. (2006). Extreme learning machine: Theory and applications, Neurocomputing, 70, 489-501.C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995.
5. libSVM. Available:http://www.csie.ntu.edu.tw/~cjlin/libsvm/.
6. R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using the second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005.
7. M.-B. Li, G.-B. Huang, P. Saratchandran, and N. Sundararajan, "Fully Complex Extreme Learning Machine," Neurocomputing, vol. 68, pp. 306-314, 2005.
8. G.-B. Huang and C.-K. Siew, "Extreme Learning Machine with Randomly Assigned RBF Kernels," International Journal of Information Technology, vol. 11, no. 1, pp. 16-24, 2005.
9. N.-Y. Liang, G.-B. Huang, P. Saratchandran, and N. Sundararajan, "A fast and accurate online sequential learning algorithm for feedforward networks," IEEE Trans. Neural Netw., vol. 17, no. 6, pp. 1411-1423, Nov. 2006.
10. Kecman, V.: Learning and Soft Computing, Support Vector machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge, MA, 2001.
11. Wang, L.P. (Ed.): Support Vector Machines: Theory and Application. Springer, Berlin, 2005.
12. Fleury M, Deflandre F, Godef roy S. Validity of Per2meability Prediction From NMR Measurements. C.R. Acad. Sci. Paris , Chimie/ Chemistry, 2001,4 (11): 869-872.
13. Vinegar H J, Waxman M H. In-situ Method for Determining Pore Size Distribution, Capillary Pressure and Permeability .U.S. Patent 4 ,644 ,283 ,1987.
14. Cancelliere A, Chang C, Foti E, Rothman D H and Succi S. The Permeability of Arandom Medium: Comparison of Simulation With Theory . Phys. Fluids A, 2 : 2085-2088.
15. Koponen A, Kandhai D, Hellén E, Alava A, Hoekstra A G, Kataja M , Niskanen K, Sloot P and Timonen K. Permeability of Three-dimensional Random Fiber Webs. Phys. Rev. Lett., 1998, 80:716-719.
16. F.E.H. Tay and L.Cao, Application of support vector machines in financial time series forecasting, Omega 29(2001) (4), pp.309-317.
17. S.S. Keerthi, Efficient tuning of SVM hyper-parameters using radius/margin bound and iterative algorithms, IEEE Transaction on Neural Networks 13(2002) (5), pp. 1225-1229.
18. P. L. Bartlett, "The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network," IEEE Transactions on Information Theory, vol.44, no.2, pp.525-536, 1998.