Trajectory Tracking of Complex Dynamical Network for Chaos Synchronization Using Recurrent Neural Network
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...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2017 |
| País: | México |
| Institución: | Universidad de Monterrey |
| Repositorio: | Redalyc-UDEM |
| OAI Identifier: | oai:redalyc.org:61552758009 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=61552758009 |
| Access Level: | acceso abierto |
| Palabra clave: | Computación work Lyapunov analysis Trajectory tracking recurrent neural net complex dynamical network |
| Sumario: | 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. |
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