文章基本信息

标题：Continuity of Chen-Fliess Series for Applications in System Identification and Machine Learning ⁎ ⁎
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作者：Rafael Dahmen ; W. Steven Gray ; Alexander Schmeding 等
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