文章基本信息

标题：Incremental Segmentation of ARX Models ⁎
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作者：Glen Chou ; Necmiye Ozay ; Dmitry Berenson 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：15
页码：587-592
DOI：10.1016/j.ifacol.2018.09.222
语种：English
出版社：Elsevier
摘要：AbstractWe consider the problem of incrementally segmenting auto-regressive models with exogenous inputs (ARX models) when the data is received sequentially at run-time. In particular, we extend a recently proposed dynamic programming based polynomial-time algorithm for offline (batch) ARX model segmentation to the incremental setting. The new algorithm enables sequential updating of the models, eliminating repeated computation, while remaining optimal. We also show how certain noise bounds can be used to detect switches automatically at run-time. The efficiency of the approach compared to the batch method is illustrated on synthetic and real data.
关键词：KeywordsSwitching autoregressive modelschange detectiondynamic programming