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Multi-Network-Feedback-Error-Learning with Automatic Insertion

  • Paulo Rogério de Almeida Ribeiro
  • Areolino de Almeida Neto
  • Alexandre César Muniz de Oliveira
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 73)

Abstract

This work is devoted to present a control application in an industrial process of iron pellet cooking in an important mining company in Brazil. This work employs an adaptive control in order to improve the performance of the conventional controller already installed in the plant. The main strategy approached here is known as Multi-Network-Feedback-Error-Learning (MNFEL). The basic idea in MNFEL is the progressive addition of neural networks in the Feedback-Error-Learning (FEL) scheme. However, this work brings innovation by proposing a mechanism of automatic insertion of new neural networks in MNFEL. In this work, due to the unknown mathematic model of the iron pellet cooking, the plant is simulated by a previously learned neural model. In such simulation environment, the proposed method is compared against conventional PID, FEL and MNFEL.

Keywords

Output Error Pelletizing Process Multiple Neural Network Teacher Signal Burner Group 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Paulo Rogério de Almeida Ribeiro
    • 1
  • Areolino de Almeida Neto
    • 1
  • Alexandre César Muniz de Oliveira
    • 1
  1. 1.Universidade Federal do Maranhão (UFMA)São LuísBrazil

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