REFERENCES
- Akcayol , M. A. 2004 . Application of adaptive neuro-fuzzy controller for SRM . Adv. Eng. Software , 35 : 129 – 137 .
- Cakmakci , M. 2007 . Adaptive neuro-fuzzy modelling of anaerobic digestion of primary sedimentation sludge . Bioproc. Biosyst. Eng. , 30 : 349 – 357 .
- Chang , F. J. and Chang , Y. T. 2006 . Adaptive neuro-fuzzy inference system for prediction of water level in reservoir . Adv. Water Resourc. , 29 : 1 – 10 .
- Christov , M. and Doh , R. 2002 . High-pressure fluid phase equilibria. Experimental methods and systems investigated (1994–999) . Fluid Phase Equilib. , 202 : 153 – 218 .
- Demuth , H. and Beale , M. 2008 . Neural network toolbox user's guide, Version 4 (Release 12). Natick , MA : The MathWorks. .
- Firat , M. and Güngör , M. 2007 . River flow estimation using adaptive neuro fuzzy inference system . Math. Comput. Sim. , 75 : 87 – 96 .
- Ganguly , S. 2003 . Prediction of VLE data using radial basis function network . Comput. Chem. Eng. , 27 : 1445 – 1454 .
- Guler , I. and Ubeyli , E. D. 2005 . Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients . J. Neurosci. Methods , 148 : 113 – 121 .
- Jang , J.-S. R. 1993 . ANFIS: Adaptive-network-based fuzzy inference system . IEEE Trans. Syst. Man Cybernet. , 23 : 665 – 685 .
- Kalyanaraman , S. B. and Akilandeswari , S. 2005 . Prediction of COD in tannery effluents: ANFIS modeling . Ind. J. Environ. Protect. , 25 : 417 – 420 .
- Karthikeyan , C. , Sabarathinam , P. L. and Aruselvi , S. 2005 . Adaptive network-based fuzzy inference system (ANFIS) modeling for wastewater treatment . Pollut. Res. , 24 : 353 – 358 .
- Laugier , S. and Richon , D. 2003 . Use of artificial neural networks for calculating derived thermodynamic quantities from volumetric property data . Fluid Phase Equilib. , 210 : 247 – 255 .
- Mohanty , S. 2005 . Estimation of vapour liquid equilibria of binary systems, carbon dioxid-ethyl caproate, ethyl caprylate and ethyl caprate using artificial neural networks . Fluid Phase Equilib. , 235 : 92 – 98 .
- Potukuchi , W. and Wexler , A. S. 1997 . Predicting vapor pressures using neural networks . Atmos. Environ. , 31 : 741 – 753 .
- Rai , P. , Majumdar , G. C. , Das Gupta , S. and De , S. 2005 . Prediction of the viscosity of clarified fruit juice using artificial neural network A combined effect of concentration and temperature . J. Food Eng. , 68 : 527 – 533 .
- Roth , H. , Peters-Gerth , P. and Lucas , K. 1992 . Experimental vapor-liquid equilibria in the systems R22-R23, R22-CO2, CS2-R22, R23-CO2, CSz-R23 and their correlation by equations of state . Fluid Phase Equilib. , 73 : 147 – 166 .
- Shyam , S. S. , Oon-Doo , B. and Michele , M. 2002 . Neural networks for predicting thermal conductivity of bakery products . J. Food Eng. , 52 : 299 – 304 .
- Soave , G. 1972 . Equilibrium constants from a modified Redlich-Kwong equation of state . Chem. Eng. Sci. , 27 : 1197 – 1203 .