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Multilayer feedforward networks are universal approximators
Neural Networks
Introduction to the theory of neural computation
Introduction to the theory of neural computation
Two strategies to avoid overfitting in feedforward networks
Neural Networks
ACM Computing Surveys (CSUR)
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IEEE Transactions on Neural Networks
Mathematics and Computers in Simulation
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Engineering Applications of Artificial Intelligence
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Environmental Modelling & Software
Expert Systems with Applications: An International Journal
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Proceedings of the 2006 conference on Artificial Intelligence Research and Development
Neural networks for estimating the efficiency of a WWTP biologic treatment
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Engineering Applications of Artificial Intelligence
A hybrid machine learning method and its application in municipal waste prediction
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
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This work is devoted to the prediction, based on neural networks, of physicochemical parameters impossible to measure on-line. These parameters-the chemical oxygen demand (COD) and the ammonia NH"4-characterize the organic matter and nitrogen removal biological process carried out at the Saint Cyprien WWTP (France). Their knowledge make it possible to estimate the process quality and efficiency. First, the data are treated by K-Means clustering then by principal components analysis (PCA) in order to optimize the multi-level perceptron (MLP) learning phase. K-Means clustering makes it possible to highlight different operations within the Saint Cyprien treatment plant. The PCA is used to eliminate redundancies and synthesizes the information expressed by a data set. With respect to the neural network used, these techniques facilitate the pollution removal process understanding and the identification of existing relations between the predictive variables and the variables to be predicted.