Chaos synchronization of coupled neurons under electrical stimulation via robust adaptive fuzzy control

Yan Qiu Che; Jiang Wang; Wai Lok Chan; Kai Ming Tsang

doi:10.1007/s11071-010-9691-9

Chaos synchronization of coupled neurons under electrical stimulation via robust adaptive fuzzy control

Yan Qiu Che, Jiang Wang, Wai Lok Chan, Kai Ming Tsang

Research output: Journal article publication › Journal article › Academic research › peer-review

27 Citations (Scopus)

Abstract

This paper presents a robust adaptive fuzzy controller to synchronize two gap junction coupled chaotic FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. A variable universe adaptive fuzzy approximator is used to approximate the nonlinear uncertain function of the synchronization error system. Based on the Lyapunov stability theory, the obtained adaptive laws of fuzzy algorithm not only guarantee the stability of the closed loop error system, but also attenuate the influence of matching error and external disturbance on synchronization error to an arbitrarily desired level. Chaos synchronization is obtained by proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method.

Original language	English
Pages (from-to)	847-857
Number of pages	11
Journal	Nonlinear Dynamics
Volume	61
Issue number	4
DOIs	https://doi.org/10.1007/s11071-010-9691-9
Publication status	Published - 1 Sept 2010

Keywords

Adaptive fuzzy control
Chaos synchronization
FitzHugh-Nagumo (FHN) model
Robust control

ASJC Scopus subject areas

Control and Systems Engineering
Aerospace Engineering
Ocean Engineering
Mechanical Engineering
Applied Mathematics
Electrical and Electronic Engineering

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10.1007/s11071-010-9691-9

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title = "Chaos synchronization of coupled neurons under electrical stimulation via robust adaptive fuzzy control",

abstract = "This paper presents a robust adaptive fuzzy controller to synchronize two gap junction coupled chaotic FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. A variable universe adaptive fuzzy approximator is used to approximate the nonlinear uncertain function of the synchronization error system. Based on the Lyapunov stability theory, the obtained adaptive laws of fuzzy algorithm not only guarantee the stability of the closed loop error system, but also attenuate the influence of matching error and external disturbance on synchronization error to an arbitrarily desired level. Chaos synchronization is obtained by proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method.",

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AU - Che, Yan Qiu

AU - Wang, Jiang

AU - Chan, Wai Lok

AU - Tsang, Kai Ming

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Y1 - 2010/9/1

N2 - This paper presents a robust adaptive fuzzy controller to synchronize two gap junction coupled chaotic FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. A variable universe adaptive fuzzy approximator is used to approximate the nonlinear uncertain function of the synchronization error system. Based on the Lyapunov stability theory, the obtained adaptive laws of fuzzy algorithm not only guarantee the stability of the closed loop error system, but also attenuate the influence of matching error and external disturbance on synchronization error to an arbitrarily desired level. Chaos synchronization is obtained by proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method.

AB - This paper presents a robust adaptive fuzzy controller to synchronize two gap junction coupled chaotic FitzHugh-Nagumo (FHN) neurons under external electrical stimulation. A variable universe adaptive fuzzy approximator is used to approximate the nonlinear uncertain function of the synchronization error system. Based on the Lyapunov stability theory, the obtained adaptive laws of fuzzy algorithm not only guarantee the stability of the closed loop error system, but also attenuate the influence of matching error and external disturbance on synchronization error to an arbitrarily desired level. Chaos synchronization is obtained by proper choice of the control parameters. The simulation results demonstrate the effectiveness of the proposed control method.

KW - Adaptive fuzzy control

KW - Chaos synchronization

KW - FitzHugh-Nagumo (FHN) model

KW - Robust control

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JO - Nonlinear Dynamics

JF - Nonlinear Dynamics

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ER -

Chaos synchronization of coupled neurons under electrical stimulation via robust adaptive fuzzy control

Abstract

Keywords

ASJC Scopus subject areas

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