Browsing by Subject "surface subsidence"

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Access status: Open Access ,
Próba zastosowania sieci neuronowych do prognozowania osiadań powierzchni terenu powstałych na skutek eksploatacji górniczej
(2006) Pawluś, Dorota
This paper presents an application of neural networks for the prediction of a surface subsidence. The main advantage of the artificial neural network approach is that there is no need to assume the type of functional relation and there is no need to have an accurate knowledge of material properties in the area of interest. Only the geometry of the neural network has to be chosen and the learning procedure has to be successfully completed. There are several types of neural network geometry. The multi-layer feed-forward networks were used for modeling the surface subsidence trough. Neural networks need to learn in order to produce useful results. There are two different kinds of learning: unsupervised learning and supervised learning. The supervised learning has been used. The networks were used as a solution to following problem. There was given excavated quadrangular area which was described by the following factors: the cordinates of vertices of a worked area, the seam thickness, the depth of the opening. We want to predicate the final subsidence of any point P(x,y). The neural networks could be used for computing the surface subsidence. The author will intend to use networks for computing the other factors of the surface deformations.
Access status: Open Access ,
Prognozowanie osiadań powierzchni terenu przy użyciu sieci neuronowych
(2007) Pawluś, Dorota
This paper presents an application of neural networks for the prediction of a surface subsidence. The main advantage of the artificial neural network approach is that there is no need to assume the type of functional relation and there is no need to have an accurate knowledge of material properties in the area of interest. Only the geometry of the neural network has to be chosen and the learning procedure has to be successfully completed. The networks were used as a solution to following problem. There was given excavated quadrangular area which was described by the following factors: the coordinates of vertices of a worked area, the seam thickness, the depth of the opening, an angle of the mining influence and the subsidence factor. We want to predict the final subsidence of any point of surface. The multi-layer feed-forward networks were used for modeling the surface subsidence trough. The supervised learning has been used. Figures 4 and 5 present the final subsidences of the points lying on two lines. The neural networks could be used for computing the surface subsidence. The author will intend to use networks for computing the other factors of the surface deformations.