Incremental synthesis of Petri net models for identification of discrete event systems

This paper addresses the problem of online identification of discrete event systems (DES). A passive method for the progressive building of Petri net (PN) models from DES outputs evolution is presented. After introducing several concepts related with dynamical properties of DES, a learning algorithm that computes ordinary PN models according to the measurement of cyclic output streams is proposed. A procedure based on this algorithm can be on-line executed tracking the DES behavior from its output signals; the successive computed models tend progressively to represent the actual observed behavior.