Neural network arithmetic of atmospheric pressure sensor and realization by MCU

HE Hong-li~1; DAN Yong-ping~2; ZHANG Yuan~3

doi:10.13873/j.1000-97872006.11.006

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Neural network arithmetic of atmospheric pressure sensor and realization by MCU

更新时间：2025-09-11

- Neural network arithmetic of atmospheric pressure sensor and realization by MCU
- Issue 11, Pages: 18-20+24(2006)
- 作者机构：
  
  1. 郑州大学物理工程学院
  2. 华中科技大学电子科学与技术系
  3. 河南工业大学
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872006.11.006
  CLC： TP212.1
- Published：2006
- 稿件说明：
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[1]何红丽,但永平,张元.气压传感器神经网络算法及单片机实现[J].传感器与微系统,2006(11):18-20+24.

HE Hong-li~1, DAN Yong-ping~2, ZHANG Yuan~3. Neural network arithmetic of atmospheric pressure sensor and realization by MCU[J]. 2006, (11): 18-20+24.
[1]何红丽,但永平,张元.气压传感器神经网络算法及单片机实现[J].传感器与微系统,2006(11):18-20+24. DOI： 10.13873/j.1000-97872006.11.006.

HE Hong-li~1, DAN Yong-ping~2, ZHANG Yuan~3. Neural network arithmetic of atmospheric pressure sensor and realization by MCU[J]. 2006, (11): 18-20+24. DOI： 10.13873/j.1000-97872006.11.006.

摘要

谐振筒式气压传感器测量范围为45

07 kPa

其输出的周期信号与气压呈非线性关系

且受环境温度影响

为了达到准确度要求（±0.05 kPa）

提出了应用神经网络算法对周期和温度信号进行数据融合

实现消除温度影响和非线性校正及在单片机上的实现方法。实验结果表明:神经网络算法对气压传感器的数据融合可行有效。

Abstract

The measure range of resonator pressure transducer is 45

07 kPa

the output period signal is nonlinear with the atmospheric pressure

and it is related to environment temperature.To meet the request of accuracy（±0.05 kPa）

the neural network arithmetic is applied to correct the nonlinear and remove the influence of environment temperature.The experimental result by MCU shows the feasibility and validity of this method.

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references

振动筒式压力传感器的FLANN非线性校正 [J]. 张铁壁,马文华. 微计算机信息 . 2005(02)

基于BP神经网络的压力传感器静态特性数据融合 [J]. 张丹,于朝民,李东. 计算机测量与控制 . 2004(06)

基于神经网络的压力传感器数据融合 [J]. 侯志祥,李河清,张玉萍. 传感器技术 . 2002(04)

MATLAB神经网络应用设计[M]. 科学出版社 , 闻新等编著, 2000

智能传感器系统[M]. 西安电子科技大学出版社 , 刘君华编著, 1999

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