Trajectory tracking of complex dynamical network for chaos synchronization using recurrent neural network

Jose P. Perez, 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 recurrent neural network and a complex dynamical network is obtained. To illustrate the analytic results we present a tracking simulation of a dynamical network with each node being just one Lorenz's dynamical system and three identical Chen's dynamical systems.

Original languageEnglish
Pages (from-to)485-492
Number of pages8
JournalComputacion y Sistemas
Volume21
Issue number3
DOIs
Publication statusPublished - 1 Sep 2017

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Recurrent neural networks
Chaos theory
Synchronization
Dynamical systems
Trajectories
Asymptotic stability

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

Cite this

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Trajectory tracking of complex dynamical network for chaos synchronization using recurrent neural network. / Perez, Jose P.; Flores H., Angel; Lopez de la Fuente, Martha S.

In: Computacion y Sistemas, Vol. 21, No. 3, 01.09.2017, p. 485-492.

Research output: Contribution to journalArticle

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