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  • 标题:Data-driven inference of conic relations via saddle-point dynamics ⁎
  • 本地全文:下载
  • 作者:Anne Romer ; Jan Maximilian Montenbruck ; Frank Allgöwer
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2018
  • 卷号:51
  • 期号:25
  • 页码:396-401
  • DOI:10.1016/j.ifacol.2018.11.139
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractConic relations of an input-output system are important system properties that can be exploited in order to design robust controllers. Therefore, we study the problem of determining the minimal cone containing such an input-output system. While in applications the input-output relation itself is often undisclosed, input-output data tuples can be sampled. Therefore, we present an iterative sampling approach to determine conic relations of a linear time-invariant system from input-output data. This sampling approach is based on saddle-point dynamics, whose convergence properties are then investigated.
  • 关键词:Keywordslearning algorithmssystem analysisiterative methodsinput-output methods
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