论文标题
数据驱动的内存依赖性动态系统的抽象
Data-driven memory-dependent abstractions of dynamical systems
论文作者
论文摘要
我们提出了一种基于样本的，顺序的方法，用于抽象一个（潜在的黑盒）动态系统，该系统具有一系列与内存相关的Markov链的序列，具有增加的大小。我们表明，这种近似允许减轻在基于样本的摘要中观察到的相关偏差。我们进一步提出了一种方法来检测苍蝇的记忆长度，导致精确的精确度。我们证明，在合理的假设下，该方法从某种意义上的意义上会收敛到声音抽象，我们在两个案例研究中展示了这一点。
We propose a sample-based, sequential method to abstract a (potentially black-box) dynamical system with a sequence of memory-dependent Markov chains of increasing size. We show that this approximation allows to alleviating a correlation bias that has been observed in sample-based abstractions. We further propose a methodology to detect on the fly the memory length resulting in an abstraction with sufficient accuracy. We prove that under reasonable assumptions, the method converges to a sound abstraction in some precise sense, and we showcase it on two case studies.
