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

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...

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Bibliographic Details
Published inNeural Nets pp. 171 - 178
Main Authors Campolo, Maurizio, Clara, Narcís, Morabito, Carlo Francesco
Format Book Chapter
LanguageEnglish
Published Berlin, Heidelberg Springer Berlin Heidelberg 2006
SeriesLecture Notes in Computer Science
Subjects
Activate Sludge Model
Hide Node
Learning Rule
Variable Selection
Wastewater Treatment Plant
Online AccessGet full text
ISBN3540331832
9783540331834
ISSN0302-9743
1611-3349
DOI10.1007/11731177_25

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Summary: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.
ISBN:3540331832
9783540331834
ISSN:0302-9743
1611-3349
DOI:10.1007/11731177_25