Synthesis of hybrid automata with affine dynamics from time-series data
Synthesis of hybrid automata with affine dynamics from time-series data
复制标题
从时间序列数据合成具有仿射动力学的混合自动机
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Christian Schilling
中科院分区：
文献类型：
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作者：
Miriam García Soto;T. Henzinger;Christian Schilling
嵌入式和网络物理系统的形式设计依赖于数学建模。在本文中，我们考虑了混合自动机的模型类别，其动力学是由仿射微分方程定义的。给定一组时间序列数据，我们提出了一种算法方法，用于合成与数据接近数据的混合自动机行为，最多可达到指定的精度，并与数据同步。我们的合成算法中的一个基本问题是检查混合自动机中时间序列的成员资格。我们的解决方案集成了仿射动力学系统的可及性和优化技术，以获得成员资格的足够和必要条件，并在改进框架中合并。该算法一次过程一次，因此可以中断，提供中间结果并恢复。我们报告了实验结果，证明了我们的合成方法的适用性。
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.