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

Mehrdad Bahrami, Mohammad Akbari, Seyed Amin Bagherzadeh, Arash Karimipour, Masoud Afrand and Marjan Goodarzi

Physica A: Statistical Mechanics and its Applications, 2019, vol. 519, issue C, 159-168

Abstract: 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.

Keywords: ANN; MSE; Training algorithms; Non-Newtonian nanofluid; Sensitivity analysis (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437118315449
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:519:y:2019:i:c:p:159-168

DOI: 10.1016/j.physa.2018.12.031

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().