Grade estimation is one of the vital stages in economic and technical investigation of mines. Therefore, to find a method which could estimate these values accurately has been considered as a necessity. In this research, an adaptive neuro fuzzy algorithm was employed in order to estimate iron grade of Skarn ores. In fact, this algorithm is a fuzzy system in which membership functions should be adjusted by training data. It is worth mentioning that definition of initial membership functions is effect on quality of output results of this algorithm. For these reason, three algorithms of grid partitioning, subtractive clustering method and fuzzy c-means clustering have been conventional in order to adjust these functions. In this paper, three neuro fuzzy systems based on aforementioned algorithms were developed. The results of test data show that there is a significant ability in neuro fuzzy system based on subtractive clustering for estimation of iron grade in comparison of other algorithms.
Majdifar, S., & Kamali, G. (2013). Iron Grade Estimation Using ANFIS Algorithm at Tappeghermez Anomaly of Sangan Mine. Journal of Analytical and Numerical Methods in Mining Engineering, 3(5), 10-17.
MLA
Saeid Majdifar; Gholamreza Kamali. "Iron Grade Estimation Using ANFIS Algorithm at Tappeghermez Anomaly of Sangan Mine". Journal of Analytical and Numerical Methods in Mining Engineering, 3, 5, 2013, 10-17.
HARVARD
Majdifar, S., Kamali, G. (2013). 'Iron Grade Estimation Using ANFIS Algorithm at Tappeghermez Anomaly of Sangan Mine', Journal of Analytical and Numerical Methods in Mining Engineering, 3(5), pp. 10-17.
VANCOUVER
Majdifar, S., Kamali, G. Iron Grade Estimation Using ANFIS Algorithm at Tappeghermez Anomaly of Sangan Mine. Journal of Analytical and Numerical Methods in Mining Engineering, 2013; 3(5): 10-17.
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