Complex dynamical network control for trajectory tracking using delayed recurrent neural networks
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 anal...
| Autores principales: | , , , |
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| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2014
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| Acceso en línea: | http://eprints.uanl.mx/15144/1/255.pdf |
| Sumario: | 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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