In this paper a neural network ap proach to the factory dynamics modelling problem is discussed. A recurrent high-order neural net work structure (RHONN) is employed to identify the manufacturing 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. Buffer states as well as control input signals are transformed into continuous ones so as to be conformant with the RHONN as sumptions. A case study demonstrates the approximation capabilities of the proposed architecture.
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