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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Campolo, M., Clara, N., Morabito, C.F. (2006). Environmental Time Series Prediction by Improved Classical Feed-Forward Neural Networks. In: Apolloni, B., Marinaro, M., Nicosia, G., Tagliaferri, R. (eds) Neural Nets. WIRN NAIS 2005 2005. Lecture Notes in Computer Science, vol 3931. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731177_25
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DOI: https://doi.org/10.1007/11731177_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33183-4
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