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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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2014
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| Online Access: | http://eprints.uanl.mx/15144/1/255.pdf |
| _version_ | 1824414276374233088 |
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| author | Pérez Padrón, José Paz Pérez Padrón, Joel Flores Hernández, Ángel Arroyo Garza, Santiago |
| author_facet | Pérez Padrón, José Paz Pérez Padrón, Joel Flores Hernández, Ángel Arroyo Garza, Santiago |
| author_sort | Pérez Padrón, José Paz |
| collection | Repositorio Institucional |
| description | 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. |
| format | Article |
| id | eprints-15144 |
| institution | UANL |
| language | English |
| publishDate | 2014 |
| record_format | eprints |
| spelling | eprints-151442021-05-20T14:55:03Z http://eprints.uanl.mx/15144/ Complex dynamical network control for trajectory tracking using delayed recurrent neural networks Pérez Padrón, José Paz Pérez Padrón, Joel Flores Hernández, Ángel Arroyo Garza, Santiago 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. 2014 Article PeerReviewed text en cc_by_nc_nd http://eprints.uanl.mx/15144/1/255.pdf http://eprints.uanl.mx/15144/1.haspreviewThumbnailVersion/255.pdf 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 http://doi.org/10.1155/2014/162610 doi:10.1155/2014/162610 |
| spellingShingle | Pérez Padrón, José Paz Pérez Padrón, Joel Flores Hernández, Ángel Arroyo Garza, Santiago Complex dynamical network control for trajectory tracking using delayed recurrent neural networks |
| thumbnail | https://rediab.uanl.mx/themes/sandal5/images/online.png |
| title | Complex dynamical network control for trajectory tracking using delayed recurrent neural networks |
| title_full | Complex dynamical network control for trajectory tracking using delayed recurrent neural networks |
| title_fullStr | Complex dynamical network control for trajectory tracking using delayed recurrent neural networks |
| title_full_unstemmed | Complex dynamical network control for trajectory tracking using delayed recurrent neural networks |
| title_short | Complex dynamical network control for trajectory tracking using delayed recurrent neural networks |
| title_sort | complex dynamical network control for trajectory tracking using delayed recurrent neural networks |
| url | http://eprints.uanl.mx/15144/1/255.pdf |
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