文章基本信息

标题：Handling Big Data in set-membership identification through a sparse optimization approach
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作者：V. Cerone ; D. Regruto
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：28
页码：1272-1278
DOI：10.1016/j.ifacol.2015.12.307
语种：English
出版社：Elsevier
摘要：AbstractThe tutorial paper discusses how to handle a large amount of data in a set- membership (SM) identification problem. First, the meaning of the term "Big data" will be clarified and explained in the considered context and, in particular, it will be shown how even a few dozen of input-output data could be "Big enough" to prevent practical estimation of the model due to the complexity of the involved optimization problems. Then, possible ad hoc solutions to properly handle the availability of large amount of data will be presented with particular attention to the peculiar sparsity structure of the considered estimation/identification problems. Numerical examples will be presented.
关键词：KeywordsBig DataSet-membership identification