DocumentCode :
3494563
Title :
Implementing neural networks onto standard low-cost microcontrollers for sensor signal processing
Author :
Medrano-Marqués, Nicolás J. ; Martín-del-Brío, Bonifacio ; Bono-Nuez, Antonio ; Bernal-Ruiz, Carlos
Author_Institution :
Fac. de Ciencias, Zaragoza Univ.
Volume :
2
fYear :
2005
fDate :
19-22 Sept. 2005
Lastpage :
972
Abstract :
In this paper we show some sensor linearizing methods based on feed-forward neural networks (multilayer perceptron). These procedures can be easily programmed into a computer-based data acquisition system. Nevertheless, we show that introducing some simplifications in the neural network architecture, these linearization procedures can also be programmed onto low-cost microcontrollers for embedded (portable) applications. In this work, we make use of an NTC sensor as a case study, but the procedure is so general and flexible that it can easily be applied to other non linear sensors. System performance measurements for both simulations and real set up are presented
Keywords :
data acquisition; feedforward neural nets; intelligent sensors; linearisation techniques; microcontrollers; signal processing; NTC sensor; computer-based data acquisition system; embedded application; feed-forward neural network; linearization; microcontroller; sensor signal processing; Application software; Computer architecture; Data acquisition; Feedforward neural networks; Feedforward systems; Microcontrollers; Multi-layer neural network; Multilayer perceptrons; Neural networks; Signal processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Emerging Technologies and Factory Automation, 2005. ETFA 2005. 10th IEEE Conference on
Conference_Location :
Catania
Print_ISBN :
0-7803-9401-1
Type :
conf
DOI :
10.1109/ETFA.2005.1612776
Filename :
1612776
Link To Document :
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