Fitting analysis and research of measured data of SAW micro-pressure sensor based on BP neural network

•We present the input and output data of the SAW micro-pressure sensor.•The work proposes two methods in contrasting the fitting measured data of the sensor.•Use BP neural network to analyze SAW micro-pressure sensor’s measured data.•BP neural network has better accuracy and fast convergence speed....

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Vydané v:Measurement : journal of the International Measurement Confederation Ročník 155; s. 107533
Hlavní autori: Li, Yuanyuan, Li, Jitong, Huang, Jian, Zhou, Hua
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Elsevier Ltd 01.04.2020
Elsevier Science Ltd
Predmet:
Back propagation
BP neural network
Data analysis
Least squares method
Micro-pressure sensor
Neural networks
Pressure sensors
Sensors
Surface acoustic wave
Surface acoustic waves
Working conditions
Micro-pressure sensor
BP neural network
Surface acoustic wave
Least squares method
ISSN:0263-2241, 1873-412X
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Popis
Shrnutí:•We present the input and output data of the SAW micro-pressure sensor.•The work proposes two methods in contrasting the fitting measured data of the sensor.•Use BP neural network to analyze SAW micro-pressure sensor’s measured data.•BP neural network has better accuracy and fast convergence speed. Sensor technology plays an important role in modern information and intelligence. The accuracy of sensor measurement becomes more challenging in complex working environment. In this paper, we studied relationship between output frequency difference data and corresponding loading pressure in SAW (Surface Acoustic Wave) micro-pressure sensor. Then using frequency difference as input and pressure as output, we construct BP (Back Propagation) neural network which is trained using experimental data and used to predict output pressure of the sensor. We also calculate error with actual loading pressure, same in the least squares method commonly used. Through multiple comparisons of same set of sample data in overall and local accuracy of predicted results, we verified that the output error predicted by BP neural network is much smaller than least squares method. For example, one set of data is only about 2.9%. It provided a new method for data analysis in SAW micro-pressure sensor.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Yuanyuan Li: Conceptualization, Methodology, Software, Resources, Formal analysis, Funding acquisition. Jitong Li: Data curation, Writing - original draft. Jian Huang: Visualization, Investigation, Supervision. Hua Zhou: Writing - review & editing.
CRediT authorship contribution statement
ISSN:0263-2241
1873-412X
DOI:10.1016/j.measurement.2020.107533