Institutional Repository
Technical University of Crete
Technical University of Crete
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| URI: | http://purl.tuc.gr/dl/dias/A14634BF-82F7-435C-92ED-202FC4AF2453 | ||
| Year | 1996 | ||
| Type of Item | Conference Full Paper | ||
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| 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 | ||
| Appears in Collections |
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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