Introduction to the theory of neural computation
Introduction to the theory of neural computation
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Advances in intelligent systems
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Optimization and simulation of secondary settler models
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Application of neural networks for seed germination assessment
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
Contribution of a simple sludge treatment in a WWTP optimization procedure
ICOSSE'06 Proceedings of the 5th WSEAS international conference on System science and simulation in engineering
Application of genetic algorithm and neural network in forecasting with good data
NN'05 Proceedings of the 6th WSEAS international conference on Neural networks
Environmental time series prediction by improved classical feed-forward neural networks
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Mathematical modelling of processes of reject water treatment in moving bed bioreactor
WSEAS TRANSACTIONS on SYSTEMS
A neo-fuzzy approach for bottom parameters estimation in oil wells
WSEAS Transactions on Systems and Control
WSEAS TRANSACTIONS on SYSTEMS
Review: Data-derived soft-sensors for biological wastewater treatment plants: An overview
Environmental Modelling & Software
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This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen.