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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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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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.
AbstractList 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.
Author Campolo, Maurizio
Morabito, Carlo Francesco
Clara, Narcís
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Nicosia, Giuseppe
Tagliaferri, Roberto
Marinaro, Maria
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Snippet 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...
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StartPage 171
SubjectTerms Activate Sludge Model
Hide Node
Learning Rule
Variable Selection
Wastewater Treatment Plant
Title Environmental Time Series Prediction by Improved Classical Feed-Forward Neural Networks
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