Suitability assessment of artificial neural network to approximate surface subsidence due to rock mass drainage
Based on the previous studies conducted by the authors, a new approach was proposed, namely the tools of artificial intelligence. One of neural networks is a multilayer perceptron network (MLP), which has already found applications in many fields of science. Sequentially, a series of calculations was made for different MLP neural network configuration and the best of them was selected. Mean square error (MSE) and the correlation coefficient R were adopted as the selection criterion for the optimal network. The obtained results were characterized with a considerable dispersion. With an increase in the amount of hidden neurons, the MSE of the network increased while the correlation coefficient R decreased. Similar conclusions were drawn for the network with a small number of hidden neurons. The analysis allowed to select a network composed of 24 neurons as the best one for the issue under question. The obtained final answers of artificial neural network were presented in a histogram as differences between the calculated and expected value.
Hejmanowski, Ryszard and Witkowski, Wojciech T.
"Suitability assessment of artificial neural network to approximate surface subsidence due to rock mass drainage,"
Journal of Sustainable Mining: Vol. 14
, Article 5.
Available at: https://doi.org/10.46873/2300-3960.1234
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