随机Petri网用时间连续Petri网流化的极限

N. Benaya, N. El-Akchioui, T. Mourabit
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引用次数: 0

摘要

可靠性分析通常基于随机离散事件模型,如马尔可夫模型或随机Petri网。对于具有多个分量的复杂动力系统,由于组合爆炸与离散模型的关系,其稳态解析表达式的求解十分繁琐。而且,随机估计量的收敛速度较慢。由于这些原因,流态化可以用来估计随机过程的渐近行为与时间连续Petri网。本文的贡献在于总结了随机和时间连续Petri网的渐近平均标记和平均吞吐量的一些性质,并指出了流化的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Limits of fluidification for a stochastic Petri nets by timed continuous Petri nets
Reliability analysis is often based on stochastic discrete event models like Markov models or stochastic Petri nets. For complex dynamical systems with numerous components, analytical expressions of the steady state are tedious to work out because of the combinatory explosion with discrete models. Moreover, the convergence of stochastic estimators is slow. For these reasons, fluidification can be investigated to estimate the asymptotic behavior of stochastic processes with timed continuous Petri nets. The contribution of this paper is to sum up some properties of the asymptotic mean marking and average throughputs of stochastic and timed continuous Petri nets, then to point out the limits of the fluidification.
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