[1] Hewitt G.F., “Measurement of two phase flow parameters”, Academic Press,London, (1978).
[2] Huang Z.Y., Wang B.L. and Li H. Q., Application of Electrical Capacitance Tomography to the Void Fraction Measurement of Two-Phase Flow, IEEE Transactions on Instrumentation and Measurement, 52 ,p. 7 (2003).
[3] Vapnik V.N., “Statistical learning theory”, Wiley,New York, (1998).
[4] Suykens J.A.K. and Vandwalle J., Least Squares Support Vector Machine Classifiers, Neural Processing Letters, 9, p. 290 (1999).
[5] Cao L.J. and H Tay F.E., Support Vector Machine with Adaptive Parameters in Financial Time Series Forecasting, IEEE Transactions on neural networks, 14, p. 1506 (2003).
[6] Wang B.L., Ji H.F., Huang Z.Y., Li H.Q., A High-Speed Data Acquisition System for ECT Based on the Differential Sampling Method, IEEE Sensors Journal, 5, p. 308 (2005).
[7] Xie D.L., Huang Z.Y., Ji H.F., Li H.Q., An Online Flow Pattern Identification System for Gas-Oil Two-Phase Flow Using Electrical Capacitance Tomography. IEEE Trans., Instrum. Meas., 55, p. 1833 (2006)
[8] Suykens J.A.K., De Brabanter J., Lukas L., Vandewalle J., Weighted Least Squares Support Vector Machines: Robustness and Sparse Approximation, Neurocomputing,48, p. 85 (2002).
[9] Chapelle O., Vapnik V.N., Bousquet O., Mukherjee S., Choosing Multiple Parameters for Support Vector Machines, Machine Learning, 46, p.131 (2002).
[10] Michalewicz, Z., “Genetic Algorithms + Data Structures = Evolution Programs”, Springer, Verlag (1992).
[11] Chuang C.C., Su S.F., Robust Support Vector Regression Networks for Function Approximation with Outliers, IEEE Transaction on Neural Networks, 13, P. 1322 (2002).
[12] Hwang J.N., Martin S.H., Martin M. R., Schimer J., Regression Modeling in Back-Propagation and Projection Pursuit Learning, IEEE Transactions on Neural Network, 5, p. 342 (1994).