Title :
Modelling the inter-stand tension of a steel cold mill based on dynamic high order neural networks
Author :
Arinton, E. ; Caraman, S.
Author_Institution :
Fac. of Electr. Eng. & Electron., Dunarea de Jos Univ., Galati, Romania
Abstract :
The paper presents a particular type of multi-layer neural networks that can be used for modelling non-linear dynamic processes. The neurons of these networks are characterized by non-linearly pre-processed inputs. Dynamic properties can be obtained by adding a filter to the neuron. The networks, built with this type of neurons, have a structure that develops during the training process in such a way that fits the complexity of the modelled system. Applications of these networks for the system identification of a complex industrial process are discussed in the final part.
Keywords :
Artificial neural networks; Milling machines; Multi-layer neural network; Neural networks; Neurons; Nonlinear dynamical systems; Predictive models; Steel; Surface fitting; System identification;
Conference_Titel :
Computational Cybernetics and Technical Informatics (ICCC-CONTI), 2010 International Joint Conference on
Conference_Location :
Timisoara, Romania
Print_ISBN :
978-1-4244-7432-5
DOI :
10.1109/ICCCYB.2010.5491325