System Identification Using Multilayer Differential Neural Networks: A New Result
In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relax...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2012-01-01
|
| Series: | Journal of Applied Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2012/529176 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850216196891213824 |
|---|---|
| author | J. Humberto Pérez-Cruz A. Y. Alanis José de Jesús Rubio Jaime Pacheco |
| author_facet | J. Humberto Pérez-Cruz A. Y. Alanis José de Jesús Rubio Jaime Pacheco |
| author_sort | J. Humberto Pérez-Cruz |
| collection | DOAJ |
| description | In previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example. |
| format | Article |
| id | doaj-art-3e7d933b042c4cc493cc103e6b4cdb52 |
| institution | OA Journals |
| issn | 1110-757X 1687-0042 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Applied Mathematics |
| spelling | doaj-art-3e7d933b042c4cc493cc103e6b4cdb522025-08-20T02:08:23ZengWileyJournal of Applied Mathematics1110-757X1687-00422012-01-01201210.1155/2012/529176529176System Identification Using Multilayer Differential Neural Networks: A New ResultJ. Humberto Pérez-Cruz0A. Y. Alanis1José de Jesús Rubio2Jaime Pacheco3Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Boulevard Marcelino García Barragán No. 1421, 44430 Guadalajara, JAL, MexicoCentro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Boulevard Marcelino García Barragán No. 1421, 44430 Guadalajara, JAL, MexicoSección de Estudios de Posgrado e Investigación, ESIME-UA, IPN, Avenida de las Granjas No. 682, 02250 Santa Catarina, NL, MexicoSección de Estudios de Posgrado e Investigación, ESIME-UA, IPN, Avenida de las Granjas No. 682, 02250 Santa Catarina, NL, MexicoIn previous works, a learning law with a dead zone function was developed for multilayer differential neural networks. This scheme requires strictly a priori knowledge of an upper bound for the unmodeled dynamics. In this paper, the learning law is modified in such a way that this condition is relaxed. By this modification, the tuning process is simpler and the dead-zone function is not required anymore. On the basis of this modification and by using a Lyapunov-like analysis, a stronger result is here demonstrated: the exponential convergence of the identification error to a bounded zone. Besides, a value for upper bound of such zone is provided. The workability of this approach is tested by a simulation example.http://dx.doi.org/10.1155/2012/529176 |
| spellingShingle | J. Humberto Pérez-Cruz A. Y. Alanis José de Jesús Rubio Jaime Pacheco System Identification Using Multilayer Differential Neural Networks: A New Result Journal of Applied Mathematics |
| title | System Identification Using Multilayer Differential Neural Networks: A New Result |
| title_full | System Identification Using Multilayer Differential Neural Networks: A New Result |
| title_fullStr | System Identification Using Multilayer Differential Neural Networks: A New Result |
| title_full_unstemmed | System Identification Using Multilayer Differential Neural Networks: A New Result |
| title_short | System Identification Using Multilayer Differential Neural Networks: A New Result |
| title_sort | system identification using multilayer differential neural networks a new result |
| url | http://dx.doi.org/10.1155/2012/529176 |
| work_keys_str_mv | AT jhumbertoperezcruz systemidentificationusingmultilayerdifferentialneuralnetworksanewresult AT ayalanis systemidentificationusingmultilayerdifferentialneuralnetworksanewresult AT josedejesusrubio systemidentificationusingmultilayerdifferentialneuralnetworksanewresult AT jaimepacheco systemidentificationusingmultilayerdifferentialneuralnetworksanewresult |