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

Authors

  • Jose P. Perez Universidad Autónoma de Nuevo León (UANL), Facultad de Ciencias Físico Matemáticas, Monterrey
  • Joel Perez Padron Universidad Autónoma de Nuevo León (UANL), Facultad de Ciencias Físico Matemáticas, Monterrey
  • Angel Flores H. UANL
  • Martha S. Lopez de la Fuente Universidad de Monterrey (UDEM), Monterrey, Nuevo León

DOI:

https://doi.org/10.13053/cys-21-3-2097

Keywords:

Fractional Complex Dynamical Systems, Trajectory Tracking, Lyapunov Theory, Control Law

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 Biography

Joel Perez Padron, Universidad Autónoma de Nuevo León (UANL), Facultad de Ciencias Físico Matemáticas, Monterrey

Profesor de tiempo completo en la Facultad de Ciencias Físico-Matemáticas de la Universidad Autonóma de Nuevo León

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Published

2017-09-28

Issue

Vol. 21 No. 3 (2017): Thematic issue: Advances in Logic-based Methods for Intelligent Systems (J. C. Nieves, E. Hermann Haeusler, B. Lopes)

Section

Articles

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