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ANNs and GAs for Predictive Controlling of Water Supply Networks
Como, Italy July 24-July 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.860799IEEE-INNS-ENNS International Joint Co ...
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M. Damas, M. Salmerón, J. Ortega, "ANNs and GAs for Predictive Controlling of Water Supply Networks," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 4, pp. 4365, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4, 2000.
 
 
BibTex
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@article{ 10.1109/IJCNN.2000.860799,
author = {M. Damas and M. Salmerón and J. Ortega},
title = {ANNs and GAs for Predictive Controlling of Water Supply Networks},
journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on},
volume = {4},
year = {2000},
issn = {1098-7576},
pages = {4365},
doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.860799},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
TI - ANNs and GAs for Predictive Controlling of Water Supply Networks
SN - 1098-7576
SP
EP
A1 - M. Damas,
A1 - M. Salmerón,
A1 - J. Ortega,
PY - 2000
VL - 4
JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
ER -
 
M. Damas, University of Granada
M. Salmerón, University of Granada
J. Ortega, University of Granada
This paper describes a procedure for controlling a water supply system. The controller uses a neural network to predict the water demand levels and a genetic algorithm to determine the feasible operation points in an optimal strategy that is based on dynamic programming. The controller has been executed in parallel in a cluster of computers. This has allowed not only the determination of the control commands in the required times but also the improvement of the control procedure performances.
Citation:
M. Damas, M. Salmerón, J. Ortega, "ANNs and GAs for Predictive Controlling of Water Supply Networks," ijcnn, vol. 4, pp.4365, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 4, 2000
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