Synthesis of Hybrid Automata with Affine Dynamics from Time-Series Data
Abstract
Formal design of embedded and cyber-physical systems relies on mathematical modeling. In this paper, we consider the model class of hybrid automata whose dynamics are defined by affine differential equations. Given a set of time-series data, we present an algorithmic approach to synthesize a hybrid automaton exhibiting behavior that is close to the data, up to a specified precision, and changes in synchrony with the data. A fundamental problem in our synthesis algorithm is to check membership of a time series in a hybrid automaton. Our solution integrates reachability and optimization techniques for affine dynamical systems to obtain both a sufficient and a necessary condition for membership, combined in a refinement framework. The algorithm processes one time series at a time and hence can be interrupted, provide an intermediate result, and be resumed. We report experimental results demonstrating the applicability of our synthesis approach.
Keywords
Cite
@article{arxiv.2102.12734,
title = {Synthesis of Hybrid Automata with Affine Dynamics from Time-Series Data},
author = {Miriam García Soto and Thomas A. Henzinger and Christian Schilling},
journal= {arXiv preprint arXiv:2102.12734},
year = {2022}
}