Radial Basis Function Network for Ore Grade Estimation |
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| Authors: | Biswajit Samanta |
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| Institution: | (1) Department of Mining Engineering, Indian Institute of Technology, Kharagpur, Kharagpur, 721302, India |
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| Abstract: | This paper highlights the performance of a radial basis function (RBF) network for ore grade estimation in an offshore placer
gold deposit. Several pertinent issues including RBF model construction, data division for model training, calibration and
validation, and efficacy of the RBF network over the kriging and the multilayer perceptron models have been addressed in this
study. For the construction of the RBF model, an orthogonal least-square algorithm (OLS) was used. The efficacy of this algorithm
was testified against the random selection algorithm. It was found that OLS algorithm performed substantially better than
the random selection algorithm. The model was trained using training data set, calibrated using calibration data set, and
finally validated on the validation data set. However, for accurate performance measurement of the model, these three data
sets should have similar statistical properties. To achieve the statistical similarity properties, an approach utilizing data
segmentation and genetic algorithm was applied. A comparative evaluation of the RBF model against the kriging and the multilayer
perceptron was then performed. It was seen that the RBF model produced estimates with the R
2 (coefficient of determination) value of 0.39 as against of 0.19 for the kriging and of 0.18 for the multilayer perceptron. |
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