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  • 标题:Continuity of Chen-Fliess Series for Applications in System Identification and Machine Learning ⁎ ⁎
  • 本地全文:下载
  • 作者:Rafael Dahmen ; W. Steven Gray ; Alexander Schmeding
  • 期刊名称:IFAC PapersOnLine
  • 印刷版ISSN:2405-8963
  • 出版年度:2021
  • 卷号:54
  • 期号:9
  • 页码:231-238
  • DOI:10.1016/j.ifacol.2021.06.080
  • 语种:English
  • 出版社:Elsevier
  • 摘要:AbstractModel continuity plays an important role in applications like system identification, adaptive control, and machine learning. This paper provides sufficient conditions under which input-output systems represented by locally convergent Chen-Fliess series are jointly continuous with respect to their generating series and as operators mapping a ball in an Lp-space to a ball in an Lq-space, where p and q are conjugate exponents. The starting point is to introduce a class of topological vector spaces known as Silva spaces to frame the problem and then to employ the concept of a direct limit to describe convergence. The proof of the main continuity result combines elements of proofs for other forms of continuity appearing in the literature to produce the desired conclusion.
  • 关键词:Keywordsnonlinear systemsChen-Fliess seriestopological vector spacessystem identificationmachine learning
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