Neural Networks for Self-tuning Control Systems

Authors

  • A. Noriega Ponce
  • A. Aguado Behar
  • A. Ordaz Hernández
  • V. Rauch Sitar

DOI:

https://doi.org/10.14311/514

Keywords:

neural networks, feedforward, back-propagation, networks, self-tuning control

Abstract

In this paper, we presented a self-tuning control algorithm based on a three layers perceptron type neural network. The proposed algorithm is advantageous in the sense that practically a previous training of the net is not required and some changes in the set-point are generally enough to adjust the learning coefficient. Optionally, it is possible to introduce a self-tuning mechanism of the learning coefficient although by the moment it is not possible to give final conclusions about this possibility. The proposed algorithm has the special feature that the regulation error instead of the net output error is retropropagated for the weighting coefficients modifications. 

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Author Biographies

A. Noriega Ponce

A. Aguado Behar

A. Ordaz Hernández

V. Rauch Sitar

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Published

2004-01-01

Issue

Section

Articles