Evaluation of multilayer perceptron and self-organizing map neural network topologies applied on microstructure segmentation from metallographic images
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Samarasinghe, 2006
Bhadeshia, 1999, Neural networks in materials science, ISIJ International, 39, 966, 10.2355/isijinternational.39.966
Bhadeshia, 2006
Miaoquan, 2004, Microstructural evolution and modelling of the hot compression of a TC6 titanium alloy, Materials Characterization, 49, 203
Kim, 2003, Prediction of welding parameters for pipeline welding using an intelligent system, The International Journal of Advanced Manufacturing Technology, 22, 713, 10.1007/s00170-003-1589-y
Kusiak, 2002, Modelling of microstructure and mechanical properties of steel using the artificial neural network, Journal of Materials Processing Technology, 127, 115, 10.1016/S0924-0136(02)00278-9
Xiao-li, 2005, Microstructure evolution model based on deformation mechanism of titanium alloy in hot forming, Transactions of nonferrous metals society of China, 15, 749
Biernacki, 2006, Prediction of properties of austempered ductile iron assisted by artificial neural network, Materials Science, 12, 11
Abdelhay, 2002, Application of artificial neural networks to predict the carbon content and the grain size for carbon steels, Egyptian Journal of Solids, 25, 229, 10.21608/ejs.2002.150480
Wang, 2007, Artificial neural network models for predicting flow stress and microstructure evolution of a hydrogenized titanium alloy, Key Engineering Materials, 353–358, 541, 10.4028/www.scientific.net/KEM.353-358.541
de Albuquerque, 2007, Image segmentation system for quantification of microstructures in metals using artificial neural networks, Revista Matéria, 12, 394
de Albuquerque, 2008, A new solution for automatic microstructures analysis from images based on a backpropagation artificial neural network, Nondestructive Testing and Evaluation, 23, 273, 10.1080/10589750802258986
Dobrzanski, 2007, Application of neural networks to classification of internal damages in steels working in creep service, Journal of Achievements in Materials and Manufacturing Engineering, 20, 303
de Santis, 2007, Optimal binarization of images by neural networks for morphological analysis of ductile cast iron, Pattern Analysis and Applications, 10, 125, 10.1007/s10044-006-0052-8
Haykin, 2009
McCullogh, 1988, A logical calculus of the ideas immanent in nervous activity, Neurocomputing: foundations of research book contents, 15
Plaut D, Nowlan SJ, Hinton GE. Experiments on learning by backpropagation. Technical Report CMU-CS, Computer Science Department, Carnegie—Mellon University; 1986.
Yin, 2005, Feature combination using boosting, Pattern Recognition Letters, 26, 2195, 10.1016/j.patrec.2005.03.029
Kohonen, 1982, Self-organized formation of topologically correct feature maps, Biological Cybernetics, 43, 59, 10.1007/BF00337288