Suitability assessment of artificial neural network to approximate surface subsidence due to rock mass drainage

• Autorzy: Hejmanowski R. , Witkowska H.

Identyfikatory

DOI

10.1016/j.jsm.2015.08.014

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

osiadanie; odwodnienie kopalni podziemnej; sztuczna sieć neuronowa; MLP; subsidence; drainage of underground mine; artificial neural networks; MLP

• Wydawca: Elsevier ; Główny Instytut Górnictwa
• Czasopismo: Journal of Sustainable Mining
• Rocznik: 2015
• Tom: Vol. 14, iss. 2
• Strony: 101--107
• Opis fizyczny: Bibliogr. 21 poz.

Twórcy

autor

Hejmanowski R.

• Faculty of Mine Surveying and Environmental Engineering, AGH University of Science and Technology, Kraków, Poland

autor

Witkowska H.

• Faculty of Mine Surveying and Environmental Engineering, AGH University of Science and Technology, Kraków, Poland

Bibliografia

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