论文标题
学习事件驱动的切换线性系统
Learning event-driven switched linear systems
论文作者
论文摘要
我们为识别其开关逻辑是事件驱动的黑框开关线性系统的识别提出了一种自动机理论学习算法。开关系统由确定性有限自动机（FA）表示，其节点标签是子系统矩阵。借助有关矩阵的尺寸和事件集的信息，以及访问两个Oracles，可以在给定输入上模拟系统，并在给出错误的假设自动机时提供反例，我们提供了输出未知FA的算法。我们的算法首先使用Oracle来获取系统的节点标签，以给定的事件输入序列运行，然后扩展Angluin's \（l^*\） - 算法来确定接受给定FA语言的FA。我们在一组基准示例中演示了学习算法的性能。
We propose an automata theoretic learning algorithm for the identification of black-box switched linear systems whose switching logics are event-driven. A switched system is expressed by a deterministic finite automaton (FA) whose node labels are the subsystem matrices. With information about the dimensions of the matrices and the set of events, and with access to two oracles, that can simulate the system on a given input, and provide counter-examples when given an incorrect hypothesis automaton, we provide an algorithm that outputs the unknown FA. Our algorithm first uses the oracle to obtain the node labels of the system run on a given input sequence of events, and then extends Angluin's \(L^*\)-algorithm to determine the FA that accepts the language of the given FA. We demonstrate the performance of our learning algorithm on a set of benchmark examples.
