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 observer when the output is corrupted by moderate level of noise. In presence of strong measurement noise, several identical complete-order observers are coupled to synchronize.
This work was supported in part by CONACyT 253677 and PAPIIT-UNAM IN113418.
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Pérez, J., Tang, Y., Grave, I. (2018). Nonlinear Observers for a Class of Neuronal Oscillators in the Presence of Strong Measurement Noise. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_84
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DOI: https://doi.org/10.1007/978-3-319-92537-0_84
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