Complex dynamical network control for trajectory tracking using delayed recurrent neural networks

Pérez Padrón, José Paz y Pérez Padrón, Joel y Flores Hernández, Ángel y Arroyo Garza, Santiago (2014) Complex dynamical network control for trajectory tracking using delayed recurrent neural networks. Mathematical Problems in Engineering, 2014. pp. 1-7. ISSN 1024-123X

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URL o página oficial: http://doi.org/10.1155/2014/162610

Resumen

In this paper, the problem of trajectory tracking is studied. Based on the V-stability and Lyapunov theory, a control law that achieves the global asymptotic stability of the tracking error between a delayed 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.

Tipo de elemento: Article
Divisiones: Ciencias Físico Matemáticas
Usuario depositante: Editor Repositorio
Creadores:
CreadorEmailORCID
Pérez Padrón, José PazNO ESPECIFICADONO ESPECIFICADO
Pérez Padrón, JoelNO ESPECIFICADONO ESPECIFICADO
Flores Hernández, ÁngelNO ESPECIFICADONO ESPECIFICADO
Arroyo Garza, SantiagoNO ESPECIFICADONO ESPECIFICADO
Fecha del depósito: 29 Mar 2019 18:45
Última modificación: 20 Mayo 2021 14:55
URI: http://eprints.uanl.mx/id/eprint/15144

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