Comparative Evaluation of Neural Network Learning Algorithms for Ore Grade Estimation

In this paper, comparative evaluation of various local and global learning algorithms in neural network modeling was performed for ore grade estimation in three deposits: gold, bauxite, and iron ore. Four local learning algorithms, standard back-propagation, back-propagation with momentum, quickprop back-propagation, and Levenberg–Marquardt back-propagation, along with two global learning algorithms, NOVEL and simulated annealing, were investigated for this purpose. The study results revealed that no benefit was achieved using global learning algorithms over local learning algorithms. The reasons for showing equivalent performance of global and local learning algorithms was the smooth error surface of neural network training for these specific case studies. However, a separate exercise involving local and global learning algorithms on a nonlinear multimodal optimization of a Rastrigin function, containing many local minima, clearly demonstrated the superior performance of global learning algorithms over local learning algorithms. Although no benefit was found by using global learning algorithms of neural network training for these specific case studies, as a safeguard against getting trapped in local minima, it is better to apply global learning algorithms in neural network training since many real-life applications of neural network modeling show local minima problems in error surface.