『(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 as seen that the RBF model produced
estimates with the R2 (coefficient of determination)
value of 0.39 as against of 0.19 for the kriging and of 0.18 for
the multilayer perceptron.
Key Words: orthogonal least square; multilayer perceptron; kriging;
genetic algorithms; offshore placer gold deposit.』
Introduction
Background
Working principles of RBF network
Selection of appropriate RBF network
Case study application
Data partition for RBF modeling
Development of the RBF model
Performance of RBF model
Summary
Acknowledgments
Appendix: Mathematical formulation of RBF network
References