Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks
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División de Estudios de Posgrado e Investigación del Instituto Tecnológico de Chihuahua. Ave. Tecnológico No. 2909, Chihuahua Chih. México Zip code: 31310
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División de Estudios de Posgrado de la Facultad de Ingeniería de la Universidad Autónoma de Querétaro. Cerro de las Campanas S/N. Col. Las campanas, Santiago de Querétaro Qro. México Zip code: 76010
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Author to whom correspondence should be addressed.
Sensors 2007, 7(8), 1509-1529; https://doi.org/10.3390/s7081509
Received: 1 June 2007
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Accepted: 10 August 2007
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Published: 16 August 2007
(This article belongs to the Special Issue Intelligent Sensors)
The development of smart sensors involves the design of reconfigurable systemscapable of working with different input sensors. Reconfigurable systems ideally shouldspend the least possible amount of time in their calibration. An autocalibration algorithmfor intelligent sensors should be able to fix major problems such as offset, variation of gainand lack of linearity, as accurately as possible. This paper describes a new autocalibrationmethodology for nonlinear intelligent sensors based on artificial neural networks, ANN.The methodology involves analysis of several network topologies and training algorithms.The proposed method was compared against the piecewise and polynomial linearizationmethods. Method comparison was achieved using different number of calibration points,and several nonlinear levels of the input signal. This paper also shows that the proposedmethod turned out to have a better overall accuracy than the other two methods. Besides,experimentation results and analysis of the complete study, the paper describes theimplementation of the ANN in a microcontroller unit, MCU. In order to illustrate themethod capability to build autocalibration and reconfigurable systems, a temperaturemeasurement system was designed and tested. The proposed method is an improvement over the classic autocalibration methodologies, because it impacts on the design process of intelligent sensors, autocalibration methodologies and their associated factors, like time and cost.
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Keywords:
intelligent sensors; reconfigurable systems; autocalibration; linearization; artificial neural network.
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MDPI and ACS Style
Rivera, J.; Carrillo, M.; Chacón, M.; Herrera, G.; Bojorquez, G. Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks. Sensors 2007, 7, 1509-1529. https://doi.org/10.3390/s7081509
AMA Style
Rivera J, Carrillo M, Chacón M, Herrera G, Bojorquez G. Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks. Sensors. 2007; 7(8):1509-1529. https://doi.org/10.3390/s7081509
Chicago/Turabian StyleRivera, José, Mariano Carrillo, Mario Chacón, Gilberto Herrera, and Gilberto Bojorquez. 2007. "Self-Calibration and Optimal Response in Intelligent Sensors Design Based on Artificial Neural Networks" Sensors 7, no. 8: 1509-1529. https://doi.org/10.3390/s7081509
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