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A recurrent neural network model to describe manufacturing cell dynamics

Rovithakis, George A., 1968-, Gaganis Vasileios, Christodoulou Manolis, Perrakis, Stelios

Πλήρης Εγγραφή


URI: http://purl.tuc.gr/dl/dias/A14634BF-82F7-435C-92ED-202FC4AF2453
Έτος 1996
Τύπος Πλήρης Δημοσίευση σε Συνέδριο
Άδεια Χρήσης
Λεπτομέρειες
Βιβλιογραφική Αναφορά 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
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Περίληψη

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.

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