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A Behavioral Framework for Data-Driven Modeling of Nonlin...
Boya Hou, Ma · 2026-05-11 · via cs.LG updates on arXiv.org

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Abstract:We generalize Jan Willems' behavioral approach to a class of discrete-time nonlinear systems in a vector-valued reproducing kernel Hilbert space (RKHS). Apart from linear time-invariant systems, this class covers nonlinear systems modeled by Volterra series and their autoregressive variants, as well as systems admitting Hammerstein-type state-space realizations. We apply the proposed framework to the problem of data-driven modeling of such systems, i.e., when simulation or control objectives for an unknown system are carried out without an explicit system identification step. To that end, we link the behavioral approach to two data-driven modeling methods in a vector-valued RKHS: (1) minimum-norm interpolation and (2) subspace identification.
Comments: 12 pages
Subjects: Systems and Control (eess.SY); Machine Learning (cs.LG)
Cite as: arXiv:2605.07052 [eess.SY]
  (or arXiv:2605.07052v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2605.07052

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Boya Hou [view email]
[v1] Fri, 8 May 2026 00:00:35 UTC (47 KB)