DocumentCode :
1803637
Title :
Artificial neural networks and data fusion as a biomass virtual sensor
Author :
Ascencio, Raul R Leal
Author_Institution :
Dept. de Electron. Sistemas e Inf., ITESO, Jalisco, Mexico
Volume :
6
fYear :
1999
fDate :
36342
Firstpage :
3968
Abstract :
The ability of artificial neural networks (ANN) to learn from experience rather than from mechanistic descriptions is making them the preferred choice to model processes with intricate variable interrelations. We apply data fusion methods (one of which is ANN) to provide estimations of biomass in a fermentation process. The readings of biomass must be periodic, of the desired frequency and reliable to a 5% error. A desired feature is that the measurement method must be robust to sensor perturbations and failures. The robustness of the presented estimator system has been tested with simulated noisy inputs and with sensor failures and a mean average error of near 5% has been obtained. A new technique is presented as a data fusion method. The technique is tested on real process data. Simulated tests are applied to evaluate performance and robustness. We demonstrated that an ANN is able to learn the interrelations between certain inputs and biomass for a fermentation process
Keywords :
fermentation; neural nets; parameter estimation; process control; scheduling; sensor fusion; biomass; data fusion; fermentation; neural networks; parameter estimation; process control; robustness; scheduling; sensor perturbations; Artificial neural networks; Biomass; Biosensors; Frequency; Robustness; Sensor fusion; Sensor phenomena and characterization; Sensor systems; Software measurement; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-5529-6
Type :
conf
DOI :
10.1109/IJCNN.1999.830792
Filename :
830792
Link To Document :
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