A hybrid method for grade estimation using genetic algorithm and neural networks |
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| Authors: | Hamid Mahmoudabadi Mohammad Izadi Mohammad Bagher Menhaj |
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| Institution: | (1) Amirkabir University of Technology, Tehran, 15914, Iran;(2) Simon Fraser University, Burnaby, V5A 1S6, Canada |
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| Abstract: | In the present paper, a new hybrid method is proposed for grade estimation. In this method, the multilayer perceptron (MLP)
network is trained using the combination of the Levenberg–Marquardt (LM) method and genetic algorithm (GA). Having a few samples
for grade estimation, it is difficult to get a proper result using some function approximation methods like neural networks
or geostatistical methods. The neural network training methods are very sensitive to initial weight values when there are
a few samples as a training dataset. The main objective of the proposed method is to resolve this problem. Here, our method
finds the optimal initial weights by combining GA and LM method. Having the optimal initial values for weights, the local
minima are avoided in the training phase and subsequently the neural network sustainability is trained optimally. Furthermore,
the hybrid method is applied for grade estimation of Gol-e-Gohar iron ore in south Iran. The proposed method shows significant
improvements compared to both conventional MLP and Kriging method. The efficiency of the proposed method gets more highlighted
when the training data set is small. |
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| Keywords: | Grade estimation Kriging Neural networks Genetic algorithm MLP Over-fitting |
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