@Article{jiot.2020.010138,
AUTHOR = {Yanming Wang, Kebin Jia, Pengyu Liu},
TITLE = {Nonlinear Correction of Pressure Sensor Based on Depth Neural Network},
JOURNAL = {Journal on Internet of Things},
VOLUME = {2},
YEAR = {2020},
NUMBER = {3},
PAGES = {109--120},
URL = {http://www.techscience.com/jiot/v2n3/40195},
ISSN = {2579-0080},
ABSTRACT = {With the global climate change, the high-altitude detection is more 
and more important in the climate prediction, and the input-output characteristic 
curve of the air pressure sensor is offset due to the interference of the tested 
object and the environment under test, and the nonlinear error is generated. 
Aiming at the difficulty of nonlinear correction of pressure sensor and the low 
accuracy of correction results, depth neural network model was established based 
on wavelet function, and Levenberg-Marquardt algorithm is used to update 
network parameters to realize the nonlinear correction of pressure sensor. The 
experimental results show that compared with the traditional neural network 
model, the improved depth neural network not only accelerates the convergence 
rate, but also improves the correction accuracy, meets the error requirements of 
upper-air detection, and has a good generalization ability, which can be extended 
to the nonlinear correction of similar sensors.},
DOI = {10.32604/jiot.2020.010138}
}