Learning nonlinear hybrid automata from input-output time-series data

Learning an automaton that approximates the behavior of a black-box system is a long-studied problem. Besides its theoretical significance, its application to search-based testing and model understanding is recently recognized. We present an algorithm to learn a nonlinear hybrid automaton (HA) that approximates a black-box hybrid system (HS) from a set of input--output traces generated by the HS. Our method is novel in handling (1) both exogenous and endogenous HS and (2) HA with reset associated with each transition. To our knowledge, ours is the first method that achieves both features. We applied our algorithm to various benchmarks and confirmed its effectiveness.