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Title:

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

Document type:
Konferenzbeitrag
Contribution type:
Textbeitrag / Aufsatz
Author(s):
Pérez, J.; Tang, Y.; Grave, I.
Pages contribution:
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...     »
Keywords:
Contraction theory; Neuro-oscillator; Observer; Synchronization
Dewey Decimal Classification:
620 Ingenieurwissenschaften
Book / Congress title:
International Symposium on Neural Networks, ISNN [15th., Minsk, 2018]
Congress (additional information):
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume:
Volume 10878 LNCS
Date of congress:
25.-28.6.2018
Publisher:
Springer Verlag
Year:
2018
Quarter:
2. Quartal
Year / month:
2018-06
Month:
Jun
Covered by:
Scopus
Print-ISBN:
978-331992536-3
Reviewed:
ja
Language:
en
Publication format:
Print
Fulltext / DOI:
doi:10.1007/978-3-319-92537-0_84
TUM Institution:
Lehrstuhl für Regelungstechnik
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