Multilayer feedforward networks are universal approximators
Neural Networks
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Reserve estimation using neural network techniques
Computers & Geosciences
Modelling soil behaviour in uniaxial strain conditions by neural networks
Advances in Engineering Software
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Simulated annealing and weight decay in adaptive learning: the SARPROP algorithm
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
The Mahalanobis-Taguchi system - Neural network algorithm for data-mining in dynamic environments
Expert Systems with Applications: An International Journal
Modeling a dynamic design system using the Mahalanobis Taguchi system: two-step optimal algorithm
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
ArcMine: A GIS extension to support mine reclamation planning
Computers & Geosciences
Application of an adaptive neuro-fuzzy inference system to ground subsidence hazard mapping
Computers & Geosciences
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Hi-index | 0.00 |
Alternatively to empirical prediction methods, methods based on influential functions and on mechanical model, artificial neural networks (ANNs) can be used for the surface subsidence prediction. In our case, the multi-layer feed-forward neural network was used. The training and testing of neural network is based on the available data. Input variables represent extraction parameters and coordinates of the points of interest, while the output variable represents surface subsidence data. After the neural network has been successfully trained, its performance is tested on a separate testing set. Finally, the surface subsidence trough above the projected excavation is predicted by the trained neural network. The applicability of ANN for the prediction of surface subsidence was verified in different subsidence models and proved on actual excavated levels and in levelled data on surface profile points in the Velenje Coal Mine.