DocumentCode :
789284
Title :
Automating Mason´s rule and its application to analysis of stochastic Petri nets
Author :
Zhou, Meng Chu ; Wang, Chi-Hsu ; Zhao, Xiaoyong
Author_Institution :
Dept. of Electr. & Comput. Eng., New Jersey Inst. of Technol., Newark, NJ, USA
Volume :
3
Issue :
2
fYear :
1995
fDate :
6/1/1995 12:00:00 AM
Firstpage :
238
Lastpage :
244
Abstract :
A symbolic performance analysis approach for discrete-event systems can be formulated based on the integration of Petri nets and moment generating function concepts. The key steps in the method include modeling a system with stochastic Petri nets, generation of state machine Petri nets with transfer functions, derivation of equivalent transfer functions, and symbolic derivation of transfer functions to obtain the performance measures. To automate the above procedure, computer implementation of Mason´s rule becomes very important for a symbolic solution. This paper proposes and implements the algorithms to evaluate the Mason´s rule and describes their applications to deriving transfer functions of a state machine Petri net for system performance. The complexity of the algorithms is analyzed. Finally, future research toward construction of a CAD tool for design of discrete-event systems is discussed
Keywords :
Petri nets; computational complexity; control system CAD; directed graphs; discrete event systems; functions; performance evaluation; stochastic automata; symbol manipulation; transfer functions; CAD tool; CIMR; Mason rule automation; computational complexity; directed graphs; discrete-event systems; equivalent transfer functions; moment generating function; state machine; stochastic Petri nets; symbolic performance analysis; system modeling; Algorithm design and analysis; Application software; Discrete event systems; Linear systems; Performance analysis; Petri nets; Stochastic processes; Stochastic systems; System performance; Transfer functions;
fLanguage :
English
Journal_Title :
Control Systems Technology, IEEE Transactions on
Publisher :
ieee
ISSN :
1063-6536
Type :
jour
DOI :
10.1109/87.388133
Filename :
388133
Link To Document :
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