Simulation and identification of Dynamic Systems through Neural Networks trained with the Error Backpropagation Method and Teacher Forcing

Authors

  • V. Leonardo Paucar Facultad de Ingeniería Eléctrica y Electrónica, Universidad Nacional de Ingeniería, Lima Perú
  • Marcos J. Rider Departamento de Sistemas de Energía Eléctrica, Universidade Estadual de Campinas
  • André L. Morelato Departamento de Sistemas de Energía Eléctrica, Universidade Estadual de Campinas , Brasil

DOI:

https://doi.org/10.21754/tecnia.v11i1.531

Abstract

This article presents the description and results of the application of the algorithm for the simulation and identification of nonlinear dynamic systems using artificial neural networks (ANN) trained with the error back-propagation method (BP back-propagation) and the teacher procedure. forcing (BPTF). Several configurations of neural networks of two layers of neurons were analyzed, one hidden and the other output. The proposed neural networks have been applied to two test systems, the double pendulum dynamic system and the third order induction motor. The results obtained allow us to estimate that the neural networks that adopt BPTF are quite useful for the simulation and identification of nonlinear dynamic systems, mainly during the first time steps after the periods with which the neural networks under study were trained.

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References

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Published

2001-06-01

How to Cite

[1]
V. Leonardo Paucar, M. J. Rider, and A. L. Morelato, “Simulation and identification of Dynamic Systems through Neural Networks trained with the Error Backpropagation Method and Teacher Forcing”, TECNIA, vol. 11, no. 1, Jun. 2001.

Issue

Vol. 11 No. 1 (2001)

Section

Articles

License

Copyright (c) 2001 TECNIA

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