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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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.

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