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arXiv 2101.07053cs.FLcs.LGcs.SE
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一种从Input/Output轨迹学习混合自动机的被动在线技术

A Passive Online Technique for Learning Hybrid Automata from Input/Output Traces

University of Tehran(德黑兰大学);Crouse Company(克劳斯公司)

Iman Saberi, Fathiyeh Faghih, Farzad Sobhi Bavil

摘要:

规范综合是从系统的输入-输出轨迹导出模型的过程。它广泛用于测试设计、逆向工程和系统辨识。对于信息物理系统来说,这一过程的一种产物是混合自动机。它们直观、精确、独立于工具,并且具有高度的抽象性,可以对具有离散和连续变量的系统进行建模。在本文中,我们提出了一种新的技术,用于合成混合自动机的输入输出轨迹的非线性信息物理系统。非线性行为中的相似性检测是提取此类模型的主要挑战。我们利用动态时间规整技术解决这个问题。我们的方法是被动的,这意味着它不需要与系统的自动机合成过程中从记录的轨迹;和在线,这意味着每个输入/输出轨迹只使用一次的过程中。换句话说,每个新的轨迹都可以用来改进已经合成的自动机。我们评估了我们的算法在两个工业和模拟案例研究。所得到的自动机的精度显示出可喜的成果。

英文摘要:

Specification synthesis is the process of deriving a model from the input-output traces of a system. It is used extensively in test design, reverse engineering, and system identification. One type of the resulting artifact of this process for cyber-physical systems is hybrid automata. They are intuitive, precise, tool independent, and at a high level of abstraction, and can model systems with both discrete and continuous variables. In this paper, we propose a new technique for synthesizing hybrid automaton from the input-output traces of a non-linear cyber-physical system. Similarity detection in non-linear behaviors is the main challenge for extracting such models. We address this problem by utilizing the Dynamic Time Warping technique. Our approach is passive, meaning that it does not need interaction with the system during automata synthesis from the logged traces; and online, which means that each input/output trace is used only once in the procedure. In other words, each new trace can be used to improve the already synthesized automaton. We evaluated our algorithm in two industrial and simulated case studies. The accuracy of the derived automata show promising results.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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