A recurrent neural network model to describe manufacturing cell dynamicsA recurrent neural network model to describe manufacturing cell dynamics Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2015-11-071996enA neural network approach to the manufacturing cell modelling problem is discussed. A recurrent high-order neural network structure (RHONN) is employed to identify cell dynamics, which is supposed to be unknown. The model is constructed in such a way that enables the design of a controller which will force the model and thus the original cell to display the required behaviour. The control input signal is transformed to a continuous one so as to conform with the RHONN assumptions, thus converting the original discrete-event system to a continuous one. A case study demonstrates the approximation capabilities of the proposed architecture.http://creativecommons.org/licenses/by/4.0/1728-173335th IEEE Conference on Decision and ControlProceedings of the 35th IEEE Conference on Decision and Control Rovithakis, George A., 1968- Gaganis Vasileios Γαγανης Βασιλειος Christodoulou Manolis Χριστοδουλου Μανωλης Perrakis, Stelios Institute of Electrical and Electronics Engineers Artificial neural networks Nets, Neural (Computer science) Networks, Neural (Computer science) Neural nets (Computer science) neural networks computer science artificial neural networks nets neural computer science networks neural computer science neural nets computer science Flexible production systems FMS (Production engineering) Manufacturing systems, Flexible Production systems, Flexible flexible manufacturing systems flexible production systems fms production engineering manufacturing systems flexible production systems flexible