A constructive neural network to predict pitting corrosion status of stainless steel

Authors:
Daniel Urda;Rafael Marcos Luque;Maria Jesus Jiménez;Ignacio Turias;Leonardo Franco;José Manuel Jerez
Affiliations:
Department of Computer Science, University of Málaga, Málaga, Spain;Department of Computer Science, University of Málaga, Málaga, Spain;Department of Civil and Industrial Engineering, University of Cádiz, Cádiz, Spain;Department of Computer Science, University of Cádiz, Cádiz, Spain;Department of Computer Science, University of Málaga, Málaga, Spain;Department of Computer Science, University of Málaga, Málaga, Spain
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Year:
2013

Citing 3
Cited 0

A “thermal” perceptron learning rule

Neural Computation
Constructive neural networks to predict breast cancer outcome by using gene expression profiles

IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
C-Mantec: A novel constructive neural network algorithm incorporating competition between neurons

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

The main consequences of corrosion are the costs derived from both the maintenance tasks as from the public safety protection. In this sense, artificial intelligence models are used to determine pitting corrosion behaviour of stainless steel. This work presents the C-MANTEC constructive neural network algorithm as an automatic system to determine the status pitting corrosion of that alloy. Several classification techniques are compared with our proposal: Linear Discriminant Analysis, k-Nearest Neighbor, Multilayer Perceptron, Support Vector Machines and Naive Bayes. The results obtained show the robustness and higher performance of the C-MANTEC algorithm in comparison to the other artificial intelligence models, corroborating the utility of the constructive neural networks paradigm in the modelling pitting corrosion problem.