论文标题
数据驱动的最快变化检测（隐藏）Markov模型
Data-Driven Quickest Change Detection in (Hidden) Markov Models
论文作者
论文摘要
本文研究了马尔可夫模型和隐藏的马尔可夫模型（HMM）中最快变化检测的问题。顺序观察取自（隐藏的）马尔可夫模型。在某些未知时间，系统发生在系统中，并更改Markov模型的过渡内核和/或HMM的发射概率。目的是快速检测变化，同时控制平均运行长度（ARL）到错误警报。研究了数据驱动的设置，其中不知道可用的后变化后分布。开发了基于内核的数据驱动算法，可以以连续状态在设置中应用，可以以递归方式进行更新，并且在计算上是有效的。在最差的平均检测延迟（WADD）上，ARL和上限的下限得出。 wadd最多是ARL对数的顺序。该算法在DC微电网和光伏系统中的两个实际问题上进一步验证。
The paper investigates the problems of quickest change detection in Markov models and hidden Markov models (HMMs). Sequential observations are taken from a (hidden) Markov model. At some unknown time, an event occurs in the system and changes the transition kernel of the Markov model and/or the emission probability of the HMM. The objective is to detect the change quickly, while controlling the average running length (ARL) to false alarm. The data-driven setting is studied, where no knowledge of the pre-, post-change distributions is available. Kernel-based data-driven algorithms are developed, which can be applied in the setting with continuous state, can be updated in a recursive fashion, and are computationally efficient. Lower bounds on the ARL and upper bound on the worst-case average detection delay (WADD) are derived. The WADD is at most of the order of the logarithm of the ARL. The algorithms are further numerically validated on two practical problems of fault detection in DC microgrid and photovoltaic systems.
