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Abstract and Applied Analysis
Volume 2012 (2012), Article ID 471281, 18 pages
http://dx.doi.org/10.1155/2012/471281
Research Article

Tracking Control Based on Recurrent Neural Networks for Nonlinear Systems with Multiple Inputs and Unknown Deadzone

1Centro Universitario de Ciencias Exactas e Ingenierías, Universidad de Guadalajara, Bulevar Marcelino García Barragán No. 1421, 44430 Guadalajara, JAL, Mexico
2Sección de Estudios de Posgrado e Investigación, ESIME UA-IPN, Avenida de las Granjas No. 682, Colonia Santa Catarina, 02250 Mexico City, DF, Mexico

Received 31 August 2012; Accepted 9 November 2012

Academic Editor: Wenchang Sun

Copyright © 2012 J. Humberto Pérez-Cruz et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [9 citations]

The following is the list of published articles that have cited the current article.

  • José de Jesús Rubio, “Evolving intelligent algorithms for the modelling of brain andeye signals,” Applied Soft Computing, 2013. View at Publisher · View at Google Scholar
  • Floriberto Ortiz Rodríguez, José Jesús Rubio, Carlos R. Mariaca Gaspar, Julio César Tovar, and Marco A. Moreno Armendáriz, “Hierarchical fuzzy CMAC control for nonlinear systems,” Neural Computing and Applications, 2013. View at Publisher · View at Google Scholar
  • José de Jesús Rubio, and J. Humberto Pérez-Cruz, “Evolving intelligent system for the modelling of nonlinear systems with dead-zone input,” Applied Soft Computing, 2013. View at Publisher · View at Google Scholar
  • Yuehjen E. Shao, Chia-Ding Hou, and Chih-Chou Chiu, “Hybrid Intelligent Modeling Schemes for Heart Disease Classification,” Applied Soft Computing, 2013. View at Publisher · View at Google Scholar
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  • José de Jesús Rubio, Zizilia Zamudio, Jaime Pacheco, and Dante Mújica Vargas, “Proportional Derivative Control with Inverse Dead-Zone for Pendulum Systems,” Mathematical Problems in Engineering, vol. 2013, pp. 1–9, 2013. View at Publisher · View at Google Scholar
  • Lei Yu, Shumin Fei, Lining Sun, Jun Huang, and Gang Yang, “Design of Robust Adaptive Neural Switching Controller for Robotic Manipulators with Uncertainty and Disturbances,” Journal of Intelligent & Robotic Systems, 2014. View at Publisher · View at Google Scholar
  • Yuehjen E. Shao, “Body Fat Percentage Prediction Using Intelligent Hybrid Approaches,” The Scientific World Journal, vol. 2014, pp. 1–8, 2014. View at Publisher · View at Google Scholar
  • S. Puga-Guzmán, J. Moreno-Valenzuela, and V. Santibáñez, “Adaptive Neural Network Motion Control of Manipulators with Experimental Evaluations,” The Scientific World Journal, vol. 2014, pp. 1–13, 2014. View at Publisher · View at Google Scholar