Corrections to "Finite buffer realization of input-output discrete event systems"

1996 ◽  
Vol 41 (4) ◽  
pp. 625-627
Author(s):  
R. Kumar ◽  
V.K. Garg ◽  
S.I. Marcus
Keyword(s):  
Discrete Event Systems ◽  
Discrete Event ◽  
Finite Buffer ◽  
Input Output ◽  
Event Systems

scholarly journals Finite buffer realization of input-output discrete-event systems

1995 ◽  
Vol 40 (6) ◽  
pp. 1042-1053 ◽  
Author(s):  
R. Kumar ◽  
V.K. Garg ◽  
S.I. Marcus
Keyword(s):  
Discrete Event Systems ◽  
Discrete Event ◽  
Finite Buffer ◽  
Input Output ◽  
Event Systems

scholarly journals Control of input/output discrete-event systems

2009 ◽  
Author(s):  
M. Petreczky ◽  
R.J.M. Theunissen ◽  
R. Su ◽  
D.A. van Beek ◽  
J. H. van Schuppen ◽  
...  
Keyword(s):  
Discrete Event Systems ◽  
Discrete Event ◽  
Input Output ◽  
Event Systems

Modeling of asynchronous discrete-event systems as networks of input-output automata

2008 ◽  
Vol 41 (2) ◽  
pp. 544-549 ◽  
Author(s):  
Sebastian Drüppel ◽  
Jan Lunze ◽  
Martin Fritz
Keyword(s):  
Discrete Event Systems ◽  
Discrete Event ◽  
Input Output ◽  
Event Systems

scholarly journals Identification of Stochastic Timed Discrete Event Systems with st-IPN

2014 ◽  
Vol 2014 ◽  
pp. 1-21 ◽  
Author(s):  
Doyra Mariela Muñoz ◽  
Antonio Correcher ◽  
Emilio García ◽  
Francisco Morant
Keyword(s):  
Petri Net ◽  
Discrete Event Systems ◽  
Discrete Event ◽  
Input Output ◽  
Identification Process ◽  
Sequence Of Events ◽  
Input And Output ◽  
Event Systems ◽  
Systems Modelling ◽  
Stochastic Time

This paper presents a method for the identification of stochastic timed discrete event systems, based on the analysis of the behavior of the input and output signals, arranged in a timeline. To achieve this goal stochastic timed interpreted Petri nets are defined. These nets link timed discrete event systems modelling with stochastic time modelling. The procedure starts with the observation of the input/output signals; these signals are converted into events, so that the sequence of events is the observed language. This language arrives to an identifier that builds a stochastic timed interpreted Petri net which generates the same language. The identified model is a deterministic generator of the observed language. The identification method also includes an algorithm that determines when the identification process is over.

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