Develop 24 dissimilar ANNs by suitable architectures & training algorithms via sensitivity analysis to better statistical presentation: Measure MSEs between targets & ANN for Fe-CuO/Eg-Water nanofluid

The artificial neural network optimization method is evaluated according to the experimental results of the hybrid non-Newtonian nanofluid of iron and copper oxide in a binary mixture of water and ethylene glycol concerned the mixture dynamic viscosity versus shear rate at different amounts of nanoparticles concentration and temperate. Present work novelty is demonstrated by providing 24 dissimilar ANN methods to introduce the suitable architectures and training algorithms for them. The mean squared errors (MSEs) between the targets and ANN outputs are evaluated to present the best optimization approach among them. Meanwhile the results would be supported by the appropriate sensitivity analysis to have better statistical visual presentation. (C) 2019 Elsevier B.V. All rights reserved.