Intellectual correction and temperature compensation of transducer based on LS-SVM

WANG Xiao-hong 1; WU De-hui 1

doi:10.13873/j.1000-97872007.03.024

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Intellectual correction and temperature compensation of transducer based on LS-SVM

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- Intellectual correction and temperature compensation of transducer based on LS-SVM
- Issue 3, Pages: 76-79(2007)
- 作者机构：
  
  1. 九江学院电子工程系
  2. 九江学院电子工程系,江西,九江,332005
  3. 合肥工业大学仪器仪表学院
  4. ,安徽,合肥,230009
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872007.03.024
  CLC： TP212.9
- Published：2007
- 稿件说明：
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[1]王晓红,吴德会.基于LS-SVM的传感器智能校正及温度补偿[J].传感器与微系统,2007(03):76-79.

WANG Xiao-hong 1, WU De-hui 1. Intellectual correction and temperature compensation of transducer based on LS-SVM[J]. 2007, (3): 76-79.
[1]王晓红,吴德会.基于LS-SVM的传感器智能校正及温度补偿[J].传感器与微系统,2007(03):76-79. DOI： 10.13873/j.1000-97872007.03.024.

WANG Xiao-hong 1, WU De-hui 1. Intellectual correction and temperature compensation of transducer based on LS-SVM[J]. 2007, (3): 76-79. DOI： 10.13873/j.1000-97872007.03.024.

摘要

提出一种基于最小二乘支持向量机（LS-SVM）的传感器非线性校正及温度补偿的新方法

并给出了相应的过程和算法。在该方法中

LS-SVM被用作构建逆模型

并通过该模型映射传感器非线性特性

同时实现了传感器的温度补偿和非线性校正。通过实际电容式压力传感器校正的实验结果表明:所提模型建模速度比SVM模型高1

个数量级

补偿误差仅为SVM模型的20%左右。因此

该学习速度快、补偿精度高、抗噪声干扰能力强

适合传感器温度补偿及校正。

Abstract

A novel method for temperature compensation and nonlinear correction of transducer based on least squares support vector machine（LS-SVM） is presented.The design steps and algorithm are also addressed.LS-SVM is used to build as an inverse model

by which the transducer nonlinear characteristic is mapped.The temperature compensation and nonlinear correction are realized synchronously.The experiment results of applying in the capacitor pressure sensor（CPS） show that the speed of this LS-SVM building model is 1～2 order of magnitude

while the compensation errors is 20 % of SVM model.As a result

the method is faster in learning speed

high in accuracy and robust in noise resistance

it is more suitable for sensor’s correction and temperature compensation.

关键词

Keywords

references

电磁力平衡传感器温度补偿的解决方案 [J]. 刘媛,方景林,邴志刚. 仪器仪表学报 . 2003(S1)

电容压力传感器的FLANN建模方法 [J]. 钱新,钱春华. 仪器仪表学报 . 2003(02)

用遗传网络校正传感器非线性误差的研究 [J]. 刘清. 计算机应用 . 2002(12)

Least Squares Support Vector Machine Classifiers [J] . J.A.K. Suykens,J. Vandewalle. Neural Processing Letters . 1999 (3)

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