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Online estimation of Hilbert-Schmidt operators and application to kernel reconstruction of neural fields

Abstract : An adaptive observer is designed for online estimation of Hilbert-Schmidt operators from online measurement of the state for some class of nonlinear infinite-dimensional dynamical systems. Convergence is ensured under detectability and persistency of excitation assumptions. The class of systems considered is motivated by an application to kernel reconstruction of neural fields, commonly used to model spatiotemporal activity of neuronal populations. Numerical simulations confirm the relevance of the approach.
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https://hal-centralesupelec.archives-ouvertes.fr/hal-03660185
Contributor : Lucas Brivadis Connect in order to contact the contributor
Submitted on : Thursday, October 6, 2022 - 11:20:26 AM
Last modification on : Wednesday, November 9, 2022 - 10:24:39 AM

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  • HAL Id : hal-03660185, version 2
  • ARXIV : 2205.03101

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Lucas Brivadis, Antoine Chaillet, Jean Auriol. Online estimation of Hilbert-Schmidt operators and application to kernel reconstruction of neural fields. CDC 2022 - 61th IEEE Conference on Decision and Control, Dec 2022, Cancun, Mexico. ⟨hal-03660185v2⟩

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