learning algorithm

In the present research, comparative evaluation of various learning algorithms in neural network modeling was performed for ore grade estimation in Sonjail porphyry copper deposit. The main goal of the following investigation would be optimizing the network architecture and to present an architectural optimization trend to better performing the copper grade estimation within the region. Therefore, 12 algorithms were investigated back propagation learning algorithms. Based on this research it is merged that by applying the LM and BFG algorithms, there would be the best performance. The reasons why the other algorithms have the same performance would be presented within the paper as well. The input parameters are coordinates and the outputs are the copper grades for each specified point. To obtain the optimal structure, a network with different layers has been applied, which it has acquired 12 neurons within one layer. To investigate the data normal shapes, various normal shape has been acquired in the [0 1], which could merged the best results. Finally to get the best network optimizations several transfer functions have been applied, and the sigmoid transfer function illustrated least error when the transfer function is selected. Considering the optimal conditions, the R2 value has merged 0.946 for network which could be the result showing that the optimal network architecture causes estimation improvement.