Ageing notions in the analysis of stochastic Petri nets
TL;DRAbstract
The present thesis introduces a method for analyzing stochastic Petri nets with arbitrarily distributed firing times. This method refers to aging properties of a stochastic Petri net. Stochastic aging properties are qualitative properties of a distribution function. A Petri net is an extended directed bipartite graph and consists of two sets P and T. The first set P contains places, which model local states of a system. The second set T consists of transitions, which model events in a system. The extension of the bipartite graph is realized by tokens, which are allocated in places. The allocation of places by tokens is denoted as a marking and it represents the global state of a Petri net. The firing of an enabled transition in a marking causes a marking change of a Petri net. The marking of a Petri net, which is given before any transition fires, is called the initial marking. If each marking of a Petri net occurs at most once, then this net is called as acyclic Petri net. In this the
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The present thesis introduces a method for analyzing stochastic Petri nets with arbitrarily distributed firing times. This method refers to aging properties of a stochastic Petri net. Stochastic aging properties are qualitative properties of a distribution function. A Petri net is an extended directed bipartite graph and consists of two sets P and T. The first set P contains places, which model local states of a system. The second set T consists of transitions, which model events in a system. The extension of the bipartite graph is realized by tokens, which are allocated in places. The allocation of places by tokens is denoted as a marking and it represents the global state of a Petri net. The firing of an enabled transition in a marking causes a marking change of a Petri net. The marking of a Petri net, which is given before any transition fires, is called the initial marking. If each marking of a Petri net occurs at most once, then this net is called as acyclic Petri net. In this the
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