Modelling the inter-stand tension of a steel cold mill based on dynamic high order neural networks

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.