that's used for grading color and comparison checking.
On the other hand, using this new proposed technique for grade estimation in this paper, the issues with FL such as definition of fuzzy if-then rules and number of MFs could be resolved.
This network is the results of applying the GA to the best obtained parameter that can be used to gain the lowest error for grade estimation and the best network subsequently.
For grade estimation, the coordinates are used as input variables, and grade attribute is used as output variable for the respective dataset.
Therefore, 35 epochs were selected for grade estimation process.
Obviously, geostatistics is one of the most prevalent techniques for grade estimation.
► We developed and applied a hybrid neural network for grade estimation.
Therefore, a new methodology is presented in this paper for grade estimation.
Besides, for the sake of comparison, we consider the performance of CANFIS GA network, ANN and ANFIS for grade estimation.
