An hybrid model through the fusion of sensitivity based linear learning method and type-2 fuzzy logic systems for modeling PVT properties of crude oil systems
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[1] Ali Selamat,et al. Constructing a Customer's Satisfactory Evaluator System Using GA-Based Fuzzy Artificial Neural Networks , 2008 .
[2] El-Sebakhy. Functional Networks as a Novel Approach for Prediction of Permeability in a Carbonate Reservoir , 2007 .
[3] Emad A. El-Sebakhy,et al. Forecasting PVT properties of crude oil systems based on support vector machines modeling scheme , 2009 .
[4] Jerry M. Mendel,et al. Type-2 fuzzy logic systems , 1999, IEEE Trans. Fuzzy Syst..
[5] J. Mendel. Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .
[6] K. A. Fattah,et al. Prediction of the PVT Data using Neural Network Computing Theory , 2003 .
[7] Jerry M. Mendel,et al. A vector similarity measure for linguistic approximation: Interval type-2 and type-1 fuzzy sets , 2008, Inf. Sci..
[8] Seyed Ali Moallemi,et al. A fuzzy logic approach for estimation of permeability and rock type from conventional well log data: an example from the Kangan reservoir in the Iran Offshore Gas Field , 2006 .
[9] Jerry M. Mendel,et al. Fuzzy sets for words: a new beginning , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..
[10] Amparo Alonso-Betanzos,et al. A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis , 2006, J. Mach. Learn. Res..
[11] Jerry M. Mendel,et al. Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).
[12] Jerry M. Mendel,et al. Connection admission control in ATM networks using survey-based type-2 fuzzy logic systems , 2000, IEEE Trans. Syst. Man Cybern. Part C.
[13] M. A. Al-Marhoun,et al. Artificial Neural Networks Models for Predicting PVT Properties of Oil Field Brines , 2005 .
[14] Ali Selamat,et al. Modeling the correlations of crude oil properties based on sensitivity based linear learning method , 2011, Eng. Appl. Artif. Intell..
[15] M. A. Al-Marhoun,et al. PVT correlations for Middle East crude oils , 1988 .
[16] J. Mendel,et al. Overcoming time-varying co-channel interference using type-2 fuzzy adaptive filters , 2000 .
[17] M. A. Al-Marhoun,et al. Using Artificial Neural Networks to Develop New PVT Correlations for Saudi Crude Oils , 2002 .
[18] Giovanni Acampora,et al. Using FML and fuzzy technology in adaptive ambient intelligent environments , 2005 .
[19] Jerry M. Mendel,et al. Type-2 fuzzy logic systems: type-reduction , 1998, SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218).
[20] Enrique F. Castillo,et al. A General Method for Local Sensitivity Analysis With Application to Regression Models and Other Optimization Problems , 2004, Technometrics.
[21] Xihao Sun,et al. Modelling redundant structure in ecosystem by type-2 fuzzy logic system☆ , 2008 .
[22] Ali Selamat,et al. Modeling the permeability of carbonate reservoir using type-2 fuzzy logic systems , 2011, Comput. Ind..
[23] Jerry M. Mendel,et al. Type-2 fuzzy sets made simple , 2002, IEEE Trans. Fuzzy Syst..
[24] Ali Selamat,et al. Modeling PVT Properties of Crude Oil Systems Using Type-2 Fuzzy Logic Systems , 2010, ICCCI.
[25] A. O. Kumoluyi. Higher-Order Neural Networks in Petroleum Engineering , 1994 .
[26] Ali Selamat,et al. Predicting correlations properties of crude oil systems using type-2 fuzzy logic systems , 2011, Expert Syst. Appl..
[27] Mohammad Hossein Fazel Zarandi,et al. A type-2 fuzzy rule-based expert system model for stock price analysis , 2009, Expert Syst. Appl..
[28] O. A. Falode,et al. PREDICTION OF NIGERIAN CRUDE OIL VISCOSITY USING ARTIFICIAL NEURAL NETWORK , 2009 .
[29] Ali Selamat,et al. An improvement on genetic-based learning method for fuzzy artificial neural networks , 2009, Appl. Soft Comput..