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)
DOI: 10.4018/978-1-5225-7598-6.ch108

Abstract

One of the ways to perform the reverse engineering of a reactive system is to analyze 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 behavior 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 behavior 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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Introduction

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.

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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.

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