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Environmental Time Series Prediction by Improved Classical Feed-Forward Neural Networks

  • Maurizio Campolo
  • Narcís Clara
  • Carlo Francesco Morabito
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3931)

Abstract

The water quality at the issue of a wastewater treatment plant (WWTP) is a complex work because of its complexity and variability when conditions suddenly change. Two main techniques has been used to improve classical feed-forward neural network. First, a classical adaptative gradient learning rule has been complemented with a Kalman learning rule which is especially effective for noisy behavioral problems. Second, two independent variable selection components -based on genetic algorithms and fuzzy ranking- have been implemented to try to improve performance and generalization. The global study shows that reliable results are obtained which permit to guarantee that neural networks are a confidence tool on this subject.

Keywords

Variable Selection Wastewater Treatment Plant Hide Node Learning Rule Activate Sludge Model 
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 2006

Authors and Affiliations

  • Maurizio Campolo
    • 1
  • Narcís Clara
    • 2
  • Carlo Francesco Morabito
    • 1
  1. 1.Dipartimento di informatica, matematica, elettronica e trasportiUniversità Mediterranea di Reggio CalabriaItaly
  2. 2.Departament d’informàtica i matemàtica aplicadaUniversitat de GironaSpain

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