- Tytuł:
- Evaluating the performance of Extreme Learning Machine technique for ore grade estimation
- Autorzy:
-
Abuntori, Clara Akalanya
Al-Hassan, Sulemana
Mireku-Gyimah, Daniel
Ziggah, Yao Yevenyo - Powiązania:
- https://bibliotekanauki.pl/articles/1839059.pdf
- Data publikacji:
- 2021
- Wydawca:
- Główny Instytut Górnictwa
- Tematy:
-
extreme learning machine
artificial intelligence
artificial neural network
grade estimation
kriging
ELM
sztuczna inteligencja
sztuczna sieć neuronowa - Opis:
- Due to the complex geology of vein deposits and their erratic grade distributions, there is the tendency of overestimating or underestimating the ore grade. These estimated grade results determine the profitability of mining the ore deposit or otherwise. In this study, five Extreme Learning Machine (ELM) variants based on hard limit, sigmoid, triangular basis, sine and radial basis activation functions were applied to predict ore grade. The motive is that the activation function has been identified to play a key role in achieving optimum ELM performance. Therefore, assessing the extent of influence the activation functions will have on the final outputs from the ELM has some scientific value worth investigating. This study therefore applied ELMas ore grade estimator which is yet to be explored in the literature. The obtained results from the five ELM variants were analysed and compared with the state-of-the-art benchmark methods of Backpropagation Neural Network (BPNN) and Ordinary Kriging (OK). The statistical test results revealed that the ELM with sigmoid activation function (ELM-Sigmoid) was the best among all the other investigated methods (ELM-Hard limit, ELM-Triangular basis, ELM-Sine, ELM-Radial Basis, BPNN and OK). This is because the ELM-sigmoid produced the lowest MAE (0.0175), MSE (0.0005) and RMSE (0.0229) with highest R2 (91.93%) and R (95.88%) respectively. It was concluded that ELM-Sigmoid can be used by field practitioners as a reliable alternative ore grade estimation technique.
- Źródło:
-
Journal of Sustainable Mining; 2021, 20, 2; 56-71
2300-1364
2300-3960 - Pojawia się w:
- Journal of Sustainable Mining
- Dostawca treści:
- Biblioteka Nauki
Artykuł