Το work with title A recurrent neural network model to describe manufacturing cell dynamics by Rovithakis, George A., 1968-, Gaganis Vasileios, Christodoulou Manolis, Perrakis, Stelios is licensed under Creative Commons Attribution 4.0 International
Bibliographic Citation
G. Rovithakis, V. Gaganis, S. Perrakis and M. Christodoulou, “A recurrent neural network model to describe manufacturing cell dynamics”, in 1996 35th IEEE Conference on Decision and Control, pp. 1728-1733. doi: 10.1109/CDC.1996.572808
https://doi.org/10.1109/CDC.1996.572808
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.