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Neural networks and correlation analysis to improve the corrosion prediction of SiO2-nanostructured patinated bronze in marine atmospheres by Henevith Méndez-Figueroa; Dario Colorado-Garrido; Miguel Hernández-Pérez; Ricardo Galván-Martínez; Ricardo Orozco Cruz is a scholarly article available to read on EtoBox.

What is Neural networks and correlation analysis to improve the corrosion prediction of SiO2-nanostructured patinated bronze in marine atmospheres about?

This article presents a new methodology that involves statistical tools and artificial neural network (ANN) modeling to predict the non-linear variation of the electrochemical impedance in two SiO 2 -nanostructured patinated quaternary bronzes in a marine atmosphere. The original experimental database provides the information on exposure time, 1⁄2Cl À ; 1⁄2SO 2 , relative humidity, precipitation level, wind speed, room temperature, the presence or absence of the nanocoating, corrosion potential, corrosion rate, frequency, and the real and imaginary parts of the impedance. All measurements were evaluated through descriptive statistical analysis and correlation matrix to find the variables that have the greatest linear influence on the imaginary impedance. For the artificial neural network modeling, the hyperbolic tangent sigmoid and radial basis in the hidden layer, and the linear transfer functions in the output layer were tested to find the best architecture using the experimental variables selected from the correlation matrix. The best-fitting training data set was obtained with 12 neurons in the input layer, 8 neurons in the hidden layer were used to achieve a coefficient of det

Author

Henevith Méndez-Figueroa; Dario Colorado-Garrido; Miguel Hernández-Pérez; Ricardo Galván-Martínez; Ricardo Orozco Cruz

Publisher

Elsevier BV

Published

2022

Language

EN