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In this paper, a computationally efficient data-driven hybrid automaton modelis proposed to capture unknown complex dynamical system behaviors usingmultiple neural networks. The sampled data of the system is divided by validpartitions into groups corresponding to their topologies and based on which,transition guards are defined. Then, a collection of small-scale neuralnetworks that are computationally efficient are trained as the local dynamicaldescription for their corresponding topologies. After modeling the system witha neural-network-based hybrid automaton, the set-valued reachability analysiswith low computation cost is provided based on interval analysis and a splitand combined process. At last, a numerical example of the limit cycle ispresented to illustrate that the developed models can significantly reduce thecomputational cost in reachable set computation without sacrificing anymodeling precision.

A Data-Driven Hybrid Automaton Framework to Modeling Complex Dynamical Systems