A recurrent neural network model to describe manufacturing cell dynamics
George A. Rovithakis, Vassilis I. Gaganis, Stelios E. Perrakis, M.A. Christodoulou · 2002
A 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.