Trajectory Tracking of Complex Dynamical Network for Chaos Synchronization Using Recurrent Neural Networks

Jose P. Perez, Joel Perez Padron, Angel Flores H., Martha S. Lopez de la Fuente

Research output: Contribution to journalArticle

Abstract

In this paper the problem of trajectory tracking is studied. Based on the Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a fractional recurrent neural network and the state of each single node of a fractional complex dynamical network is obtained. To illustrate the analytic results we present a tracking simulation of a simple network with four different nodes and five non-uniform links.
Original languageUndefined/Unknown
JournalComputacion y Sistemas
Volume21
Issue number3
DOIs
Publication statusPublished - 1 Sep 2017

Cite this

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title = "Trajectory Tracking of Complex Dynamical Network for Chaos Synchronization Using Recurrent Neural Networks",
abstract = "In this paper the problem of trajectory tracking is studied. Based on the Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a fractional recurrent neural network and the state of each single node of a fractional complex dynamical network is obtained. To illustrate the analytic results we present a tracking simulation of a simple network with four different nodes and five non-uniform links.",
author = "Perez, {Jose P.} and {Perez Padron}, Joel and {Flores H.}, Angel and {Lopez de la Fuente}, {Martha S.}",
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journal = "Computacion y Sistemas",
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publisher = "Centro de Investigacion en Computacion (CIC) del Instituto Politecnico Nacional (IPN)",
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Trajectory Tracking of Complex Dynamical Network for Chaos Synchronization Using Recurrent Neural Networks. / Perez, Jose P.; Perez Padron, Joel; Flores H., Angel; Lopez de la Fuente, Martha S.

In: Computacion y Sistemas, Vol. 21, No. 3, 01.09.2017.

Research output: Contribution to journalArticle

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T1 - Trajectory Tracking of Complex Dynamical Network for Chaos Synchronization Using Recurrent Neural Networks

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AU - Perez Padron, Joel

AU - Flores H., Angel

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AB - In this paper the problem of trajectory tracking is studied. Based on the Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a fractional recurrent neural network and the state of each single node of a fractional complex dynamical network is obtained. To illustrate the analytic results we present a tracking simulation of a simple network with four different nodes and five non-uniform links.

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