Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Universal Approximation of Flows of Control Systems by Recurrent Neural Networks
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Reglerteknik. (Digital Futures)ORCID-id: 0009-0006-0657-4103
Eindhoven University of Technology, Control Systems Group, EE Dept., MB Eindhoven, The Netherlands.
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Reglerteknik. KTH, Skolan för elektroteknik och datavetenskap (EECS), Centra, ACCESS Linnaeus Centre. (Digital Futures)ORCID-id: 0000-0001-9940-5929
2023 (engelsk)Inngår i: 2023 62nd IEEE Conference on Decision and Control, CDC 2023, Institute of Electrical and Electronics Engineers (IEEE) , 2023, s. 2320-2327Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We consider the problem of approximating flow functions of continuous-time dynamical systems with inputs. It is well-known that continuous-time recurrent neural networks are universal approximators of this type of system. In this paper, we prove that an architecture based on discrete-time recurrent neural networks universally approximates flows of continuous-time dynamical systems with inputs. The required assumptions are shown to hold for systems whose dynamics are well-behaved ordinary differential equations and with practically relevant classes of input signals. This enables the use of off-the-shelf solutions for learning such flow functions in continuous-time from sampled trajectory data.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE) , 2023. s. 2320-2327
Emneord [en]
Machine learning, Neural networks, Nonlinear systems
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-343710DOI: 10.1109/CDC49753.2023.10383457ISI: 001166433801150Scopus ID: 2-s2.0-85184831372OAI: oai:DiVA.org:kth-343710DiVA, id: diva2:1839905
Konferanse
62nd IEEE Conference on Decision and Control, CDC 2023, Singapore, Singapore, Dec 13 2023 - Dec 15 2023
Merknad

QC 20240222

Part of ISBN  979-8-3503-0124-3

Tilgjengelig fra: 2024-02-22 Laget: 2024-02-22 Sist oppdatert: 2024-03-26bibliografisk kontrollert

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekstScopus

Person

Aguiar, MiguelJohansson, Karl H.

Søk i DiVA

Av forfatter/redaktør
Aguiar, MiguelJohansson, Karl H.
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 26 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf