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Micromachines 2015, 6(5), 554-573; doi:10.3390/mi6050554

Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements

1,2,3,†,* , 3,†
,
1,2,3
,
4,* , 1,3
and
1,3
1
Jiangsu Key Laboratory of Meteorological Observation and information Processing, Nanjing University of Information Science and Technology, Nanjing 210044, China
2
Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
3
School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
4
School of Physics and Optoelectronic Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editor: Stefano Mariani
Received: 4 February 2015 / Revised: 21 April 2015 / Accepted: 29 April 2015 / Published: 6 May 2015
View Full-Text   |   Download PDF [4947 KB, uploaded 6 May 2015]   |  

Abstract

This paper provides a novel and effective compensation method by improving the hardware design and software algorithm to achieve optimization of piezoresistive pressure sensors and corresponding measurement systems in order to measure pressure more accurately and stably, as well as to meet the application requirements of the meteorological industry. Specifically, GE NovaSensor MEMS piezoresistive pressure sensors within a thousandth of accuracy are selected to constitute an array. In the versatile compensation method, the hardware utilizes the array of MEMS pressure sensors to reduce random error caused by sensor creep, and the software adopts the data fusion technique based on the wavelet neural network (WNN) which is improved by genetic algorithm (GA) to analyze the data of sensors for the sake of obtaining accurate and complete information over the wide temperature and pressure ranges. The GA-WNN model is implemented in hardware by using the 32-bit STMicroelectronics (STM32) microcontroller combined with an embedded real-time operating system µC/OS-II to make the output of the array of MEMS sensors be a direct digital readout. The results of calibration and test experiments clearly show that the GA-WNN technique can be effectively applied to minimize the sensor errors due to the temperature drift, the hysteresis effect and the long-term drift because of aging and environmental changes. The maximum error of the low cost piezoresistive MEMS-array pressure transmitter proposed by us is within 0.04% of its full-scale value, and it can satisfy the meteorological pressure measurement. View Full-Text
Keywords: high-precision; array of MEMS pressure sensors; data fusion; wavelet neural network; genetic algorithm; temperature drift compensation; hysteresis compensation; long-term stability; hardware implementation of GA-WNN model high-precision; array of MEMS pressure sensors; data fusion; wavelet neural network; genetic algorithm; temperature drift compensation; hysteresis compensation; long-term stability; hardware implementation of GA-WNN model
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhang, J.; Wu, Y.; Liu, Q.; Gu, F.; Mao, X.; Li, M. Research on High-Precision, Low Cost Piezoresistive MEMS-Array Pressure Transmitters Based on Genetic Wavelet Neural Networks for Meteorological Measurements. Micromachines 2015, 6, 554-573.

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