A new global optimization for hybrid automaton Identification

System identification to a hybrid automaton is a complex and flourishing research field involving expertise in both continuous dynamics and discrete event system modeling. Current methodologies for hybrid automaton identification predominantly rely on a framework centered around the sequential resolution of local signal processing optimization problems. This paper seeks to enhance the existing framework by introducing a novel step designed to address certain limitations inherent in the non-global optimization aspect of a sequential resolution framework. The proposed approach enables each identification step not only to address its local optimization challenges but also to contribute to the optimization of a global cost function including model distance, sequential fidelity, and automaton structure.