Data modeling and compensation in high-pressure dynamic calibration

GONG Chi-kun1; LI Yong-xin2

doi:10.13873/j.1000-97872008.07.027

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Data modeling and compensation in high-pressure dynamic calibration

更新时间：2025-09-11

- Data modeling and compensation in high-pressure dynamic calibration
- Issue 7, Pages: 59-61+65(2008)
- 作者机构：
  
  1. 上海理工大学机械工程学院
  2. 南京理工大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872008.07.027
  CLC： TP212
- Published：2008
- 稿件说明：
移动端阅览
[1]宫赤坤,李永新.高压动态校准的数据建模与补偿[J].传感器与微系统,2008(07):59-61+65.

GONG Chi-kun1, LI Yong-xin2. Data modeling and compensation in high-pressure dynamic calibration[J]. 2008, (7): 59-61+65.
[1]宫赤坤,李永新.高压动态校准的数据建模与补偿[J].传感器与微系统,2008(07):59-61+65. DOI： 10.13873/j.1000-97872008.07.027.

GONG Chi-kun1, LI Yong-xin2. Data modeling and compensation in high-pressure dynamic calibration[J]. 2008, (7): 59-61+65. DOI： 10.13873/j.1000-97872008.07.027.

摘要

提出采用小波包相关阈值法对测量数据进行去噪

对处理后的数据采用基于减法聚类和LMS算法的RBF神经网络来建立并补偿传感器模型。研究结果表明:小波包相关阈值法去噪优于常规的去噪方法

所提出的算法简单、收敛速度快、建立的模型精度高

并使传感器的非线性得到了有效的校正。

Abstract

The correlative threshold denoising of wavelet packet transformation is used to process the test data

and the RBF neural network with subtractive clustering and LMS algorithm is used to establish and compensate the transducer model.The study results show that the network structure is simple and astringency is fast

the modeling and compensation by using the new algorithm is effective to correct the nonlinear dynamic character of transducer

and noise elimination with correlative threshold plus wavelet packet transformation is superior to conventional noise elimination methods.

关键词

Keywords

references

A fast training algorithm for RBF networks based on subtractive clustering [J] . Haralambos Sarimveis,Alex Alexandridis,George Bafas. Neurocomputing . 2003

A novel cluster based MLSE equalizer for M-PAM signaling schemes [J] . Yannis Kopsinis,Sergios Theodoridis. Signal Processing . 2003 (9)

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