Paper Info
Title
Design and Identification of Stochastic and Deterministic Stochastic Petri Nets
Abstract
In this paper, we consider the identification problem of stochastic and deterministic stochastic Petri nets (PNs). The approach herein proposed consists of inferring a PN structure and identifying its parameters. Hence, the first step leads to the synthesis of a PN structure with the measurable sequence of events and states. This approach determines the measurable part and estimates the nonmeasurable part of the PN to be established. Once both parts are obtained, the PN structure and the initial marking of the nonmeasurable places are obtained thanks to the integer linear programming technique. In the second step of this approach, the parameters of the obtained model are estimated. Stochastic and deterministic stochastic PNs with deterministic and exponentially distributed transition durations are considered. A systematic identification method is proposed based on event sequences that are recorded by supervision systems. This method is based on a Markov model whose state space is isomorphic to the reachability graph of the untimed PN model.
Year
DOI
Venue
2012
10.1109/TSMCA.2011.2173798
IEEE Transactions on Systems, Man, and Cybernetics, Part A
Keywords
Field
DocType
markov model,parameter estimation,markov process,markov processes,artificial neural network,state space,artificial neural networks,stochastic petri net,exponential distribution,identification,vectors,upper bound,linear programming,sensors,petri nets,integer programming,petri net
Petri net,Markov process,Computer science,Markov model,Stochastic Petri net,Reachability,Integer programming,Artificial intelligence,State space,Machine learning,Parameter identification problem
Journal
Volume
Issue
ISSN
42
4
1083-4427
Citations 
PageRank 
References 
7
0.53
15
Authors
6
Name
Order
Citations
PageRank
Souleiman Ould el Mehdi170.53
Rebiha Bekrar270.53
Nadhir Messai3194.13
Edouard Leclercq48712.49
Dimitri Lefebvre536252.36
Bernard Riera6248.31