Identification of Petri Net Models Based on an Asymptotic Approach

The identification problem considered in this work, consists in compute an Interpreted Petri Net (IPN) model, in proportion as new output signals of the system are observed. The identification problem becomes complex when the complete state of the system cannot be fully measured. The state information that is not observed is inferred during the identification process allowing the computed model represents the observed system behavior. As the system evolves new information is revealed and the wrong dependencies are eliminated in order to update the computed model. Given this problem, in this paper are presented the needed algorithms to identify a class of Petri Nets (PN) known as state machines.