Document Type
Article
Publication Date
2021
DOI
10.1016/j.ifacol.2021.06.080
Publication Title
IFAC-PapersOnLine
Volume
54
Issue
9
Pages
231-238
Abstract
Model 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.
Original Publication Citation
Dahmen, R., Gray, W. S., & Schmeding, A. (2021). Continuity of Chen-Fliess series for applications in system identification and machine learning. IFAC-PapersOnLine, 54(9), 231-238. https://doi.org/10.1016/j.ifacol.2021.06.080
Repository Citation
Dahmen, Rafael; Gray, W. Steven; and Schmeding, Alexander, "Continuity of Chen-Fliess Series for Applications in System Identification and Machine Learning" (2021). Electrical & Computer Engineering Faculty Publications. 299.
https://digitalcommons.odu.edu/ece_fac_pubs/299
Included in
Artificial Intelligence and Robotics Commons, Systems and Communications Commons, Systems Engineering Commons, Systems Science Commons
Comments
© 2021 The Authors. This is an open access article under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.