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THE GLOBAL NEURAL NETWORK LEARNING USING GENETIC ALGORITHM FOR ORE GRADE PREDICTION OF AN IRON ORE DEPOSIT

BROWSE_DETAIL_CREATION_DATE: 2013

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BROWSE_DETAIL_TYPE: Article

BROWSE_DETAIL_PUBLISH_STATE: Published

BROWSE_DETAIL_FORMAT: PDF Document

BROWSE_DETAIL_LANG: English

BROWSE_DETAIL_SUBJECTS: Ore deposits and mining of particular metals,

BROWSE_DETAIL_CREATORS: CHATTERJEE, SNEHAMOY (Author), BANDOPADHYAY, SUKUMAR (Author),

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BROWSE_DETAIL_PUBLISHER: Atılım Üniversitesi BROWSE_DETAIL_PUBLICATION_NAME: The International Journal of Mineral Resources Engineering BROWSE_DETAIL_PUBLICATION_LOCATION: Ankara BROWSE_DETAIL_PUBLICATION_DATE: 2007 BROWSE_DETAIL_PUBLICATION_NUMBER: 4 BROWSE_DETAIL_PUBLICATION_VOLUME: 12 BROWSE_DETAIL_PUBLICATION_PAGE: 217-240


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In this paper, genetic algorithm is used for training the neural network model for ore

grade prediction of an iron ore mine located in India. The network model incorporated

the spatial locations as well as the lithological information as input parameters for

the neural network model. A genetic algorithm was incorporated to find a neural

network solution close to the global optimum. A pre-established topology was used

for this case study. The network topology constituted three layers: an input, an output

and a hidden layer. The input layer consisted of three spatial coordinates (x, y and z)

and seven litho-types. The output layer is comprised of the Fe grade. The optimum

connection weights and biased terms were obtained by a genetic algorithm search

process. The numbers of hidden nodes were selected by searching with all possible

hidden nodes size. The optimum number of hidden nodes, however, was obtained

based on the minimum error of training data. To justify the use of GA-based neural

network modeling, a comparative evaluation between Levenberg-Marquardt neural

network learning, ordinary kriging, and lognormal kriging method was performed.

The comparative evaluation of these algorithms suggests that the GA-based neural

network model determinedly outperformed the other models. The grade tonnage curve

was then developed using the GA neural network model. Before developing the grade

tonnage curve, it was required to develop the lithological maps of the deposit at the

unknown grid points. Indicator kriging was used for developing the lithological maps

of the deposit.


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