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System Identification Using Multilayer Differential Neural Networks: A New Result

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  • J. Humberto Pérez-Cruz
  • A. Y. Alanis
  • José de Jesús Rubio
  • Jaime Pacheco

Abstract

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.

Suggested Citation

  • J. Humberto Pérez-Cruz & A. Y. Alanis & José de Jesús Rubio & Jaime Pacheco, 2012. "System Identification Using Multilayer Differential Neural Networks: A New Result," Journal of Applied Mathematics, Hindawi, vol. 2012, pages 1-20, April.
    • Handle: RePEc:hin:jnljam:529176
    DOI: 10.1155/2012/529176
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