Results 211 to 220 of about 19,047 (229)

Design and Identification of Stochastic and Deterministic Stochastic Petri Nets

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2012
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.
Ould El Mehdi, Souleiman, Bekrar, R., Messai, N., Leclercq, E., Lefebvre, D., Riera, B.   +5 more
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Introduction to Stochastic Petri Nets

2001
Stochastic Petri Nets are a modelling formalism that can be conveniently used for the analysis of complex models of Discrete Event Dynami Systems (DEDS) and for their performance and reliability evaluation. The automatic construction of the probabilistic models that underly the dynamic behaviours of these nets rely on a set of results that derive from ...
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Tagged Generalized Stochastic Petri Nets

2009
This paper introduces an extension of the Generalized Stochastic Petri Net (GSPN) formalism in order to enable the computation of first passage time distributions of tokens. A "tagged token" technique is used which relies on net's structural properties to guide the correct specification of this extension.
BALBO, Gianfranco, DE PIERRO, Massimiliano, FRANCESCHINIS, Giuliana   +2 more
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Analysis of discrete‐time stochastic petri nets

Statistica Neerlandica, 2000
The Petri net formalism is widely applied in both theoretical and practical settings. For the sake of performance analysis, the original Petri net model has been extended with the notion of time. This paper addresses the different issues involved with this extension.
Hajo A. Reijers, van der Wmp Wil Aalst, van Km Kees Hee   +2 more
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Stochastic Petri Nets and Their Applications

2001
Although continuous-time Markov chains have been widely used to analyze the performance of communication networks, constructing and solving a continuous-time Markov chain is a tedious and error-prone procedure, especially when the systems are complex. A relief from the burden is provided by stochastic Petri nets and the corresponding software packages,
Kishor S. Trivedi, Yonghuan Cao, Yue Ma, Hairong Sun   +3 more
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History-Dependent Stochastic Petri Nets

2010
Stochastic Petri Nets are a useful and well-known tool for performance analysis. However, an implicit assumption in the different types of Stochastic Petri Nets is the Markov property. It is assumed that a choice in the Petri net only depends on the current state and not on earlier choices.
Helen Schonenberg, Natalia Sidorova, Wil M. P. van der Aalst, Kees M. van Hee   +3 more
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Markov regenerative stochastic Petri nets

Performance Evaluation, 1994
Abstract Stochastic Petri nets of various types (SPN, GSPN, ESPN, DSPN etc.) are recognized as useful modeling tools for analyzing the performance and reliability of systems. The analysis of such Petri nets proceeds by utilizing the underlying continuous-time stochastic processes — continuous-time Markov chains for SPN and GSPN, semi-Markov processes
Kishor S. Trivedi, Vidyadhar G. Kulkarni, Hoon Choi   +2 more
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Arbitrary Stochastic Petri Nets

1998
Recall Section 5.1.3. When the firing policy for an STPN is the race with enabling age memory (R-E), the marginal distributions in (5.5) are the residual life distributions of t k conditioned to age variable a k :
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Colored Stochastic Petri Nets

2002
Use of the standard set of SPN building blocks to model very large or complex systems can sometimes result in nets that have an enormous number of places and transitions. One popular strategy for obtaining more concise specifications in such cases is to associate “colors” with both tokens and transitions and to work with “colored stochastic Petri nets”
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Modelling with Stochastic Petri Nets

2002
Stochastic Petri nets (SPNs) are well suited to representing concurrency, synchronization, precedence, and priority. After presenting the basic SPN building blocks in Section 2.1, we give a series of examples in Section 2.2 that illustrates the use of SPNs for modelling discrete-event systems.
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