Petri nets design based on neural networks
نویسندگان
چکیده
Petri net faulty models are useful for reliability analysis and fault diagnosis of discrete event systems. Such models are difficult to work out as long as they must be computed according to alarm propagation. This paper deals with Petri net models synthesis and identification based on neural network approaches, with regard to event propagation and to state propagation dataset. A learning neural algorithm is proposed to build Petri net models, these models are suitable for the diagnosis of discrete event systems.
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