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Titel:

Nonlinear observers for a class of neuronal oscillators in the presence of strong measurement noise

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Pérez, J.; Tang, Y.; Grave, I.
Seitenangaben Beitrag:
pp. 736-744
Abstract:
The objective of this paper is to design observers for a class of neuronal oscillators on the one hand, and to give a comparative study of the observer performance as the number of synchronized observer increases, on the other hand. More specifically, we apply the methodology of observer design in [4] for a class of neural oscillators. Contraction tool [7] is applied to obtain an exponentially convergent reduced-order observer, which serves as a building-block to construct a complete-order obser...     »
Stichworte:
Contraction theory; Neuro-oscillator; Observer; Synchronization
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Kongress- / Buchtitel:
International Symposium on Neural Networks, ISNN [15th., Minsk, 2018]
Kongress / Zusatzinformationen:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band / Teilband / Volume:
Volume 10878 LNCS
Datum der Konferenz:
25.-28.6.2018
Verlag / Institution:
Springer Verlag
Jahr:
2018
Quartal:
2. Quartal
Jahr / Monat:
2018-06
Monat:
Jun
Nachgewiesen in:
Scopus
Print-ISBN:
978-331992536-3
Reviewed:
ja
Sprache:
en
Erscheinungsform:
Print
Volltext / DOI:
doi:10.1007/978-3-319-92537-0_84
TUM Einrichtung:
Lehrstuhl für Regelungstechnik
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