Title :
Investigation of back-propagation artificial neural networks in modelling the stress-strain behaviour of sandstone rock
Author :
Millar, David ; Clarici, E.
Author_Institution :
Dept. of Mineral Resources Eng., Imperial Coll. of Sci., Technol. & Med., London, UK
fDate :
27 Jun-2 Jul 1994
Abstract :
Highlights the current principal application areas of artificial neural networks in mineral engineering. An investigation into the suitability of a multilayer perceptron architecture using the generalised delta training rule with backpropagation of error for synthesising deformability models for Crosland Sandstone is discussed. The results of the investigation indicate that this method is effective in achieving this objective for engineering purposes
Keywords :
backpropagation; digital simulation; geophysics computing; mechanical engineering; mechanical engineering computing; multilayer perceptrons; rocks; stress-strain relations; Crosland Sandstone; backpropagation artificial neural networks; deformability model synthesis; elastic property; elasticity; error backpropagation; generalised delta training rule; geophysics computing; mineral engineering; model; multilayer perceptron architecture; rock mechanics; sandstone rock; stress-strain behaviour; stress-strain behaviour modelling; Artificial neural networks; Deformable models; Educational institutions; Geologic measurements; Geology; Intelligent networks; Mineral resources; Multilayer perceptrons; Neural networks; Ores;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374770
