Open Access

ARTICLE

Nonlinear Correction of Pressure Sensor Based on Depth Neural Network

Yanming Wang^1,2,3, Kebin Jia^1,2,3,*, Pengyu Liu^1,2,3

1 Beijing University of Technology, Beijing, 100124, China
2 Beijing Laboratory of Advanced Information Networks, Beijing, 100124, China
3 Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

* Corresponding Author: Kebin Jia. Email:

Journal on Internet of Things 2020, 2(3), 109-120. https://doi.org/10.32604/jiot.2020.010138

Received 18 March 2020; Accepted 25 June 2020; Issue published 16 September 2020

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.

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Keywords

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