  In this paper, a computationally efficient data-driven hybrid automaton model
is proposed to capture unknown complex dynamical system behaviors using
multiple neural networks. The sampled data of the system is divided by valid
partitions into groups corresponding to their topologies and based on which,
transition guards are defined. Then, a collection of small-scale neural
networks that are computationally efficient are trained as the local dynamical
description for their corresponding topologies. After modeling the system with
a neural-network-based hybrid automaton, the set-valued reachability analysis
with low computation cost is provided based on interval analysis and a split
and combined process. At last, a numerical example of the limit cycle is
presented to illustrate that the developed models can significantly reduce the
computational cost in reachable set computation without sacrificing any
modeling precision.
