Thermal conductivity and dynamic viscosity modeling of Fe₂O₃/water nanofluid by applying various connectionist approaches

References

• B. Lamas, B. Abreu, A. Fonseca, N. Martins, and M. Oliveira, “Numerical analysis of percolation formation in carbon nanotube based nanofluids,” Int. J. Numer. Methods Eng., vol. 95, no. 3, pp. 257–270, 2013. DOI: 10.1002/nme.4510.
   Web of Science ®Google Scholar
• S. Parvin and A. J. Chamkha, “An analysis on free convection flow, heat transfer and entropy generation in an odd-shaped cavity filled with nanofluid,” Int. Commun. Heat Mass Transf., vol. 54, pp. 8–17, 2014. DOI: 10.1016/J.ICHEATMASSTRANSFER.2014.02.031.
   Web of Science ®Google Scholar
• S. Parvin, R. Nasrin, M. A. Alim, N. F. Hossain, and A. J. Chamkha, “Thermal conductivity variation on natural convection flow of water–alumina nanofluid in an annulus,” Int. J. Heat Mass Transf., vol. 55, no. 19–20, pp. 5268–5274, 2012. DOI: 10.1016/J.IJHEATMASSTRANSFER.2012.05.035.
   Web of Science ®Google Scholar
• T. E. Amin, G. Roghayeh, R. Fatemeh, and P. Fatollah, “Evaluation of nanoparticle shape effect on a nanofluid based flat-plate solar collector efficiency,” Energy Explor. Exploit., vol. 33, no. 5, pp. 659–676, 2015. DOI: 10.1260/0144-5987.33.5.659.
   Web of Science ®Google Scholar
• M. A. Nazari, R. Ghasempour, M. H. Ahmadi, G. Heydarian, and M. B. Shafii, “Experimental investigation of graphene oxide nanofluid on heat transfer enhancement of pulsating heat pipe,” Int. Commun. Heat Mass Transf., vol. 91, pp. 90–94, 2018. DOI: 10.1016/j.icheatmasstransfer.2017.12.006.
   Web of Science ®Google Scholar
• M. M. Tawfik, “Experimental studies of nanofluid thermal conductivity enhancement and applications: a review,” Renew. Sustain. Energy Rev., vol. 75, pp. 1239–1253, 2017. DOI: 10.1016/J.RSER.2016.11.111.
   Web of Science ®Google Scholar
• S. Ponmani, J. K. M. William, R. Samuel, R. Nagarajan, and J. S. Sangwai, “Formation and characterization of thermal and electrical properties of CuO and ZnO nanofluids in xanthan gum,” Colloids Surf. A: Physicochem. Eng. Aspects, vol. 443, pp. 37–43, 2014. DOI: 10.1016/j.colsurfa.2013.10.048.
   Web of Science ®Google Scholar
• O. A. Alawi, N. A. C. Sidik, H. W. Xian, T. H. Kean, and S. N. Kazi, “Thermal conductivity and viscosity models of metallic oxides nanofluids,” Int. J. Heat Mass Transf., vol. 116, pp. 1314–1325, 2018. DOI: 10.1016/J.IJHEATMASSTRANSFER.2017.09.133.
   Web of Science ®Google Scholar
• W. Cui, Z. Shen, J. Yang, and S. Wu, “Molecular dynamics simulation on flow behaviors of nanofluids confined in nanochannel,” Case Stud. Therm. Eng., vol. 5, pp. 114–121, 2015. DOI: 10.1016/j.csite.2015.03.007.
   Web of Science ®Google Scholar
• A. J. Chamkha, S. Abbasbandy, A. M. Rashad, and K. Vajravelu, “Radiation effects on mixed convection about a cone embedded in a porous medium filled with a nanofluid,” Meccanica, vol. 48, no. 2, pp. 275–285, 2013. DOI: 10.1007/s11012-012-9599-1.
   Web of Science ®Google Scholar
• A. Gandomkar, M. H. Saidi, M. B. Shafii, M. Vandadi, and K. Kalan, “Visualization and comparative investigations of pulsating ferro-fluid heat pipe,” Appl. Therm. Eng., vol. 116, pp. 56–65, 2017. DOI: 10.1016/J.APPLTHERMALENG.2017.01.068.
   Web of Science ®Google Scholar
• M. Kahani, S. Z. Heris, and S. M. Mousavi, “Effects of curvature ratio and coil pitch spacing on heat transfer performance of Al2O3/water nanofluid laminar flow through helical coils,” J. Dispers. Sci. Technol., vol. 34, no. 12, pp. 1704–1712, 2013. DOI: 10.1080/01932691.2013.764485.
   Web of Science ®Google Scholar
• S. Akilu, A. T. Baheta, and K. V. Sharma, “Experimental measurements of thermal conductivity and viscosity of ethylene glycol-based hybrid nanofluid with TiO2-CuO/C inclusions,” J. Mol. Liq., vol. 246, pp. 396–405, 2017. DOI: 10.1016/J.MOLLIQ.2017.09.017.
   Web of Science ®Google Scholar
• M. Bahiraei and S. M. Hosseinalipour, “Thermal dispersion model compared with Euler-Lagrange approach in simulation of convective heat transfer for nanoparticle suspensions,” J. Dispers. Sci. Technol., vol. 34, no. 12, pp. 1778–1789, 2013. DOI: 10.1080/01932691.2012.751339.
   Web of Science ®Google Scholar
• A. J. Chamkha and A. M. Rashad, “Natural convection from a vertical permeable cone in a nanofluid saturated porous media for uniform heat and nanoparticles volume fraction fluxes,” Int. J. Numer. Methods Heat Fluid Flow, vol. 22, no. 8, pp. 1073–1085, 2012. DOI: 10.1108/09615531211271871.
   Web of Science ®Google Scholar
• Z. T. Tabari and S. Z. Heris, “Heat transfer performance of milk pasteurization plate heat exchangers using MWCNT/water nanofluid,” J. Dispers. Sci. Technol., vol. 36, no. 2, pp. 196–204, 2015. DOI: 10.1080/01932691.2014.894917.
   Web of Science ®Google Scholar
• M. R. Salimpour, A. Abdollahi, and M. Afrand, “An experimental study on deposited surfaces due to nanofluid pool boiling: comparison between rough and smooth surfaces,” Exp. Therm. Fluid Sci., vol. 88, pp. 288–300, 2017. DOI: 10.1016/J.EXPTHERMFLUSCI.2017.06.007.
   Web of Science ®Google Scholar
• X. Fang et al., “Heat transfer and critical heat flux of nanofluid boiling: a comprehensive review,” Renew. Sustain. Energy Rev., vol. 62, pp. 924–940, 2016. DOI: 10.1016/J.RSER.2016.05.047.
   Web of Science ®Google Scholar
• A. V. Minakov, M. I. Pryazhnikov, D. V. Guzei, G. M. Zeer, and V. Y. Rudyak, “The experimental study of nanofluids boiling crisis on cylindrical heaters,” Int. J. Therm. Sci., vol. 116, pp. 214–223, 2017. DOI: 10.1016/J.IJTHERMALSCI.2017.02.019.
   Web of Science ®Google Scholar
• M. Dadjoo, N. Etesami, and M. N. Esfahany, “Influence of orientation and roughness of heater surface on critical heat flux and pool boiling heat transfer coefficient of nanofluid,” Appl. Therm. Eng., vol. 124, pp. 353–361, 2017. DOI: 10.1016/J.APPLTHERMALENG.2017.06.025.
   Web of Science ®Google Scholar
• T.-K. Hong, H.-S. Yang, and C. J. Choi, “Study of the enhanced thermal conductivity of fe nanofluids,” J. Appl. Phys., vol. 97, no. 6, p. 064311, 2005. DOI: 10.1063/1.1861145.
   Web of Science ®Google Scholar
• J. Garg et al., “Enhanced thermal conductivity and viscosity of copper nanoparticles in ethylene glycol nanofluid,” J. Appl. Phys., vol. 103, no. 7, p. 074301, 2008. DOI: 10.1063/1.2902483.
   Web of Science ®Google Scholar
• S. Kannaiyan et al., “Comparison of experimental and calculated thermophysical properties of alumina/cupric oxide hybrid nanofluids,” J. Mol. Liq., vol. 244, pp. 469–477, 2017. DOI: 10.1016/j.molliq.2017.09.035.
   Web of Science ®Google Scholar
• M. Zadkhast, D. Toghraie, and A. Karimipour, “Developing a new correlation to estimate the thermal conductivity of MWCNT-CuO/water hybrid nanofluid via an experimental investigation,” J. Therm. Anal. Calorim., vol. 129, no. 2, pp. 859–867, 2017. DOI: 10.1007/s10973-017-6213-8.
   Web of Science ®Google Scholar
• M. Sheikholeslami and D. D. Ganji, “Numerical modeling of magnetohydrodynamic CuO—water transportation inside a porous cavity considering shape factor effect,” Colloids Surf. A: Physicochem. Eng. Aspects, vol. 529, pp. 705–714, 2017. DOI: 10.1016/j.colsurfa.2017.06.046.
   Web of Science ®Google Scholar
• M. H. Esfe and M. H. Hajmohammad, “Thermal conductivity and viscosity optimization of nanodiamond-Co3O4/EG (40:60) aqueous nanofluid using NSGA-II coupled with RSM,” J. Mol. Liq., vol. 238, pp. 545–552, 2017. DOI: 10.1016/J.MOLLIQ.2017.04.056.
   Web of Science ®Google Scholar
• A. A. Abdullah, S. A. Althobaiti, and K. A. Lindsay, “Marangoni convection in water–alumina nanofluids: dependence on the nanoparticle size,” Eur. J. Mech. – B/Fluids, vol. 67, pp. 259–268, 2018. DOI: 10.1016/J.EUROMECHFLU.2017.09.015.
   Web of Science ®Google Scholar
• D. Toghraie, V. A. Chaharsoghi, and M. Afrand, “Measurement of thermal conductivity of ZnO–TiO2/EG hybrid nanofluid,” J. Therm. Anal. Calorim., vol. 125, no. 1, pp. 527–535, 2016. DOI: 10.1007/s10973-016-5436-4.
   Web of Science ®Google Scholar
• S. Z. Heris, M. Shokrgozar, S. Poorpharhang, M. Shanbedi, and S. H. Noie, “Experimental study of heat transfer of a car radiator with CuO/ethylene glycol-water as a coolant,” J. Dispers. Sci. Technol., vol. 35, no. 5, pp. 677–684, 2014. DOI: 10.1080/01932691.2013.805301.
   Web of Science ®Google Scholar
• M. H. Esfe, H. Rostamian, M. Reza Sarlak, M. Rejvani, and A. Alirezaie, “Rheological behavior characteristics of TiO2-MWCNT/10w40 hybrid nano-oil affected by temperature, concentration and shear rate: an experimental study and a neural network simulating,” Physica E: Low-Dimens. Syst. Nanostruct., vol. 94, pp. 231–240, 2017. DOI: 10.1016/J.PHYSE.2017.07.012.
   Web of Science ®Google Scholar
• M. H. Esfe, M. R. H. Ahangar, M. Rejvani, D. Toghraie, and M. H. Hajmohammad, “Designing an artificial neural network to predict dynamic viscosity of aqueous nanofluid of TiO2 using experimental data,” Int. Commun. Heat Mass Transf., vol. 75, pp. 192–196, 2016. DOI: 10.1016/J.ICHEATMASSTRANSFER.2016.04.002.
   Web of Science ®Google Scholar
• S. A. Sadatsakkak, M. H. Ahmadi, and M. A. Ahmadi, “Implementation of artificial neural-networks to model the performance parameters of Stirling engine,” Mech. Ind., vol. 17, no. 3, p. 307, 2016. DOI: 10.1051/meca/2015062.
   Web of Science ®Google Scholar
• M. Gholipour Khajeh, A. Maleki, M. A. Rosen, and M. H. Ahmadi, “Electricity price forecasting using neural networks with an improved iterative training algorithm,” Int. J. Ambient Energy, vol. 39, no. 2, pp. 147–158, 2018. DOI: 10.1080/01430750.2016.1269674.
   Web of Science ®Google Scholar
• M. H. Ahmadi et al., “Prediction of performance of Stirling engine using least squares support machine technique,” Mech. Ind., vol. 17, no. 5, p. 506, 2016. DOI: 10.1051/meca/2015098.
   Web of Science ®Google Scholar
• S. M. Pourkiaei, M. H. Ahmadi, and S. M. Hasheminejad, “Modeling and experimental verification of a 25W fabricated PEM fuel cell by parametric and GMDH-type neural network,” Mech. Ind., vol. 17, no. 1, p. 105, 2016. DOI: 10.1051/meca/2015050.
   Web of Science ®Google Scholar
• M. H. Esfe, H. Rostamian, M. R. Sarlak, M. Rejvani, and A. Alirezaie, “Rheological behavior characteristics of TiO2-MWCNT/10w40 hybrid nano-oil affected by temperature, concentration and shear rate: an experimental study and a neural network simulating,” Physica E: Low-Dimens. Syst. Nanostruct., vol. 94, pp. 231–240, 2017.
   Web of Science ®Google Scholar
• S. Kortesis and P. D. Panagiotopoulos, “Neural networks for computing in structural analysis: Methods and prospects of applications,” Int. J. Numer. Methods Eng., vol. 36, no. 13, pp. 2305–2318, 1993. DOI: 10.1002/nme.1620361310.
   Web of Science ®Google Scholar
• S. Yoshimura, A. Matsuda, and G. Yagawa, “New regularization by transformation for neural network based inverse analyses and its application to structure identification,” Int. J. Numer. Methods Eng., vol. 39, no. 23, pp. 3953–3968, 1996. DOI: 10.1002/(SICI)1097-0207(19961215)39:23 < 3953::AID-NME31 > 3.0.CO;2-O.
   Web of Science ®Google Scholar
• J. Ghaboussi, D. A. Pecknold, M. Zhang, and R. M. Haj-Ali, “Autoprogressive training of neural network constitutive models,” Int. J. Numer. Methods Eng., vol. 42, no. 1, pp. 105–126, 1998. DOI: 10.1002/(SICI)1097-0207(19980515)42:1 < 105::AID-NME356 > 3.0.CO;2-V.
   Web of Science ®Google Scholar
• A. A. Nadooshan, M. H. Esfe, and M. Afrand, “Prediction of rheological behavior of SiO2-MWCNTs/10W40 hybrid nanolubricant by designing neural network,” J. Therm. Anal. Calorim., vol. 131, no. 3, pp. 2741–2748, 2018. DOI: 10.1007/s10973-017-6688-3.
   Web of Science ®Google Scholar
• A. Alirezaie, S. Saedodin, M. H. Esfe, and S. H. Rostamian, “Investigation of rheological behavior of MWCNT (COOH-functionalized)/MgO-Engine Oil hybrid nanofluids and modelling the results with artificial neural networks,” J. Mol. Liq., vol. 241, pp. 173–181, 2017. DOI: 10.1016/J.MOLLIQ.2017.05.121.
   Web of Science ®Google Scholar
• M. H. Esfe, S. Esfande, and S. H. Rostamian, “Experimental evaluation, new correlation proposing and ANN modeling of thermal conductivity of ZnO-DWCNT/EG hybrid nanofluid for internal combustion engines applications,” Appl. Therm. Eng., vol. 133, pp. 452–463, 2018. DOI: 10.1016/J.APPLTHERMALENG.2017.11.131.
   Web of Science ®Google Scholar
• M. H. Esfe, S. Esfandeh, S. Saedodin, and H. Rostamian, “Experimental evaluation, sensitivity analyzation and ANN modeling of thermal conductivity of ZnO-MWCNT/EG-water hybrid nanofluid for engineering applications,” Appl. Therm. Eng., vol. 125, pp. 673–685, 2017. DOI: 10.1016/J.APPLTHERMALENG.2017.06.077.
   Web of Science ®Google Scholar
• M. Afrand, M. H. Esfe, E. Abedini, and H. Teimouri, “Predicting the effects of magnesium oxide nanoparticles and temperature on the thermal conductivity of water using artificial neural network and experimental data,” Physica E: Low-Dimens. Syst. Nanostruct., vol. 87, pp. 242–247, 2017. DOI: 10.1016/j.physe.2016.10.020.
   Web of Science ®Google Scholar
• M. H. Esfe, M. Rejvani, R. Karimpour, and A. A. A. Arani, “Estimation of thermal conductivity of ethylene glycol-based nanofluid with hybrid suspensions of SWCNT-Al2O3 nanoparticles by correlation and ANN methods using experimental data,” J. Therm. Anal. Calorim., vol. 128, no. 3, pp. 1359–1371, 2017. DOI: 10.1007/s10973-016-6002-9.
   Web of Science ®Google Scholar
• M. Shamaeil, M. Firouzi, and A. Fakhar, “The effects of temperature and volume fraction on the thermal conductivity of functionalized DWCNTs/ethylene glycol nanofluid,” J. Therm. Anal. Calorim., vol. 126, no. 3, pp. 1455–1462, 2016. DOI: 10.1007/s10973-016-5548-x.
   Web of Science ®Google Scholar
• M. H. Esfe, S. Esfandeh, and M. Rejvani, “Modeling of thermal conductivity of MWCNT-SiO2 (30:70%)/EG hybrid nanofluid, sensitivity analyzing and cost performance for industrial applications,” J. Therm. Anal. Calorim., vol. 131, no. 2, pp. 1437–1447, 2018. DOI: 10.1007/s10973-017-6680-y.
   Web of Science ®Google Scholar
• M. H. Esfe, A. A. A. Arani, R. S. Badi, and M. Rejvani, “ANN modeling, cost performance and sensitivity analyzing of thermal conductivity of DWCNT–SiO2/EG hybrid nanofluid for higher heat transfer,” J. Therm. Anal. Calorim., vol. 131, no. 3, pp. 2381–2393, 2018. DOI: 10.1007/s10973-017-6744-z.
   Web of Science ®Google Scholar
• M. Vakili, M. Karami, S. Delfani, S. Khosrojerdi, and K. Kalhor, “Experimental investigation and modeling of thermal conductivity of CuO–water/EG nanofluid by FFBP-ANN and multiple regressions,” J. Therm. Anal. Calorim., vol. 129, no. 2, pp. 629–637, 2017. DOI: 10.1007/s10973-017-6217-4.
   Web of Science ®Google Scholar
• M. R. Esfahani, E. M. Languri, and M. R. Nunna, “Effect of particle size and viscosity on thermal conductivity enhancement of graphene oxide nanofluid,” Int. Commun. Heat Mass Transf., vol. 76, pp. 308–315, 2016. DOI: 10.1016/J.ICHEATMASSTRANSFER.2016.06.006.
   Web of Science ®Google Scholar
• C. H. Li and G. P. Peterson, “The effect of particle size on the effective thermal conductivity of Al2O3-water nanofluids,” J. Appl. Phys., vol. 101, no. 4, p. 044312, 2007. DOI: 10.1063/1.2436472.
   Web of Science ®Google Scholar
• M. H. Ahmadi, M. A. Ahmadi, M. A. Nazari, O. Mahian, and R. Ghasempour, “A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach,” J. Therm. Anal. Calorim., pp. 1–11, 2018. DOI: 10.1007/s10973-018-7035-z.
   Google Scholar
• M. H. Ahmadi et al., “Thermal conductivity ratio prediction of Al2O3/water nanofluid by applying connectionist methods,” Colloids Surf. A: Physicochem. Eng. Aspects, vol. 541, pp. 154–164, 2018. DOI: 10.1016/J.COLSURFA.2018.01.030.
   Web of Science ®Google Scholar
• M. Gardner and S. Dorling, “Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences,” Atmos. Environ., vol. 32, no. 14–15, pp. 2627–2636, 1998. DOI: 10.1016/S1352-2310(97)00447-0.
   Web of Science ®Google Scholar
• K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Netw., vol. 2, no. 5, pp. 359–366, 1989. DOI: 10.1016/0893-6080(89)90020-8.
   Web of Science ®Google Scholar
• U. Orhan, M. Hekim, and M. Ozer, “EEG signals classification using the K-means clustering and a multilayer perceptron neural network model,” Expert Syst. Appl., vol. 38, no. 10, pp. 13475–13481, 2011. DOI: 10.1016/J.ESWA.2011.04.149.
   Web of Science ®Google Scholar
• D. W. Ruck, S. K. Rogers, M. Kabrisky, M. E. Oxley, and B. W. Suter, “The multilayer perceptron as an approximation to a Bayes optimal discriminant function,” IEEE Trans. Neural Netw., vol. 1, no. 4, pp. 296–298, 1990. DOI: 10.1109/72.80266.
   PubMedGoogle Scholar
• E. Vanzella et al., “Photometric redshifts with the multilayer perceptron neural network: application to the HDF-S and SDSS,” A&A, vol. 423, no. 2, pp. 761–776, 2004. DOI: 10.1051/0004-6361:20040176.
   Web of Science ®Google Scholar
• H. M. Goda, E. M. El-M Shokir, K. A. Fattah, and M. H. Sayyouh, “Prediction of the PVT data using neural network computing theory,” Niger. Annu. Int. Conf. Exhib., Society of Petroleum Engineers, 2003. DOI: 10.2118/85650-MS.
   Google Scholar
• N. Sundararajan, P. Saratchandran, and Y. W. Lu, Radial Basis Function Neural Networks with Sequential Learning: MRAN and Its Applications. Singapore: World Scientific, 1999.
   Google Scholar
• T. Sayahi, A. Tatar, and M. Bahrami, “A RBF model for predicting the pool boiling behavior of nanofluids over a horizontal rod heater,” Int. J. Therm. Sci., vol. 99, pp. 180–194, 2016. DOI: 10.1016/J.IJTHERMALSCI.2015.08.010.
   Web of Science ®Google Scholar
• A. Tatar et al., “Prediction of carbon dioxide solubility in aqueous mixture of methyldiethanolamine and N-methylpyrrolidone using intelligent models,” Int. J. Greenh. Gas Control, vol. 47, pp. 122–136, 2016. DOI: 10.1016/J.IJGGC.2016.01.048.
   Web of Science ®Google Scholar
• J. Park and I. W. Sandberg, “Universal approximation using radial-basis-function networks,” Neural Comput., vol. 3, no. 2, pp. 246–257, 1991. DOI: 10.1162/neco.1991.3.2.246.
   PubMed Web of Science ®Google Scholar
• A. G. Bors, “Introduction to the radial basis function (RBF) networks,” Online Symp. Electron. Eng., 2001.
   Google Scholar
• S. Chen, C. F. N. Cowan, and P. M. Grant, “Orthogonal least squares learning algorithm for radial basis function networks,” IEEE Trans. Neural Netw., vol. 2, no. 2, pp. 302–309, 1991. DOI: 10.1109/72.80341.
   PubMed Web of Science ®Google Scholar
• A. Tatar et al., “Implementing radial basis function neural network for prediction of surfactant retention in petroleum production and processing industries,” Pet. Sci. Technol., vol. 34, no. 11–12, pp. 992–999, 2016. DOI: 10.1080/10916466.2016.1177548.
   Web of Science ®Google Scholar
• A. Tatar et al., “Implementing radial basis function neural networks for prediction of saturation pressure of crude oils,” Pet. Sci. Technol., vol. 34, no. 5, pp. 454–463, 2016. DOI: 10.1080/10916466.2016.1141217.
   Web of Science ®Google Scholar
• C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. 20, no. 3, pp. 273–297, 1995. DOI: 10.1007/BF00994018.
   Web of Science ®Google Scholar
• E. D. Übeyli, “Least squares support vector machine employing model-based methods coefficients for analysis of EEG signals,” Expert Syst. Appl., vol. 37, no. 1, pp. 233–239, 2010. DOI: 10.1016/J.ESWA.2009.05.012.
   Web of Science ®Google Scholar
• T. van Gestel et al., “Benchmarking least squares support vector machine classifiers,” Mach. Learn., vol. 54, no. 1, pp. 5–32, 2004. DOI: 10.1023/B:MACH.0000008082.80494.e0.
   Web of Science ®Google Scholar
• K. Pelckmans et al., LS-SVMlab: a MATLAB/C toolbox for least squares support vector machines, 2002.
   Google Scholar
• A. Hemmati-Sarapardeh et al., “Reservoir oil viscosity determination using a rigorous approach,” Fuel, vol. 116, pp. 39–48, 2014. DOI: 10.1016/J.FUEL.2013.07.072.
   Web of Science ®Google Scholar
• J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, Least Squares Support Vector Machines. Singapore: World Scientific, 2002. DOI: 10.1142/5089.
   Google Scholar
• H. Wang and D. Hu, Comparison of SVM and LS-SVM for Regression. 2005 Int. Conf. Neural Networks Brain, vol.1, IEEE; n.d., p. 279–83. DOI: 10.1109/ICNNB.2005.1614615.+++
   Google Scholar
• S. Rafiee-Taghanaki et al., “Implementation of SVM framework to estimate PVT properties of reservoir oil,” Fluid Phase Equilib., vol. 346, pp. 25–32, 2013. DOI: 10.1016/J.FLUID.2013.02.012.
   Web of Science ®Google Scholar
• M. H. Esfe, A. Tatar, M. R. H. Ahangar, and H. Rostamian, “A comparison of performance of several artificial intelligence methods for predicting the dynamic viscosity of TiO2/SAE 50 nano-lubricant,” Physica E: Low-Dimens. Syst. Nanostruct., vol. 96, pp. 85–93, 2018.
   Web of Science ®Google Scholar
• T. Van Gestel et al., “Bayesian framework for least-squares support vector machine classifiers, Gaussian processes, and kernel Fisher discriminant analysis,” Neural Comput., vol. 14, no. 5, pp. 1115–1147, 2002. DOI: 10.1162/089976602753633411.
   PubMed Web of Science ®Google Scholar
• A. Najafi-Marghmaleki, A. Barati-Harooni, A. Tatar, A. Mohebbi, and A. H. Mohammadi, “On the prediction of Watson characterization factor of hydrocarbons,” J. Mol. Liq., vol. 231, pp. 419–429, 2017. DOI: 10.1016/J.MOLLIQ.2017.01.098.
   Web of Science ®Google Scholar
• H. Shayeghi and A. Ghasemi, “Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme,” Energy Convers. Manag., vol. 74, pp. 482–491, 2013. DOI: 10.1016/J.ENCONMAN.2013.07.013.
   Web of Science ®Google Scholar
• Y.-K. Chan and Y.-C. Tsai, “Multiple regression approach to predict turbine-generator output for Chinshan nuclear power plant,” Kerntechnik, vol. 82, no. 1, pp. 24–30, 2017. DOI: 10.3139/124.110647.
   Web of Science ®Google Scholar