Petri Nets Identification Techniques for Automated Modelling of Discrete Event Processes

Petri Nets Identification Techniques for Automated Modelling of Discrete Event Processes

Edelma Rodriguez-Perez (CINVESTAV Unidad Guadalajara, Mexico) and Ernesto Lopez-Mellado (CINVESTAV Unidad Guadalajara, Mexico)
Copyright: © 2018 |Pages: 15
DOI: 10.4018/978-1-5225-2255-3.ch652


One of the ways to perform the reverse engineering of a reactive system is to analyse the model of such a system. However, this model could not exist or the documentation could not be updated; then a model that describes the current behaviour of the system has to be built. Automated modelling of reactive discrete event processes can be achieved through identification techniques, which yield suitable discrete event models from the observed behaviour in the form of input-output sequences. This chapter presents an overview of input-output identification techniques that build Petri net models.
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Building Petri net models from system behaviour observations is a hard task when the system is large and complex; then, the use of computer-aided modelling tools is useful. Identification techniques have been useful for building systematically models involving events and states. Finite automata and Petri nets have been used as a formalism to describe the functioning of discrete event processes in operation.

Reactive systems are embedded within an environment interacting with other systems. We focus on systems that interact through binary signals, which is the case of discrete event processes. The behaviour of the system is then captured as sequences of vectors whose entries are the values of input-output signals; afterwards, the sequences are processed by an identification method to obtain the discrete event model. This is shown in Figure 1.

Figure 1.


This chapter surveys relevant identification methods and overviews two approaches that generate models of different levels of abstraction; one that describes in detail the relationship between input events and outputs, and other that yields compact descriptions. Finally, current research problems and trends on discrete event process identification are discussed.

Key Terms in this Chapter

Discrete Event Model: Accurate representation of the system behaviour expressed in a fine formalism, usually a finite automaton or a Petri net.

Discrete Event Systems: A class of dynamical systems in which the behaviour is characterised by successions of steady states delimited by events in general asynchronous.

Identification Methods: Techniques that build systematically formal models from the observation of external behaviour.

Input-Output Identification: Identification approach based on input-output sequences observed from a discrete event process.

Petri Nets: Formalism for specifying discrete event systems behaviours, allowing describing states, events, causal and concurrent relations, information exchange, resource allocation, and other complex behaviours.

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