Modeling the sensor calibration based on RNN with feature extraction

Hui Zhang; Yong Ni; Yue Chang

doi:10.1117/12.819583

31 December 2008 Modeling the sensor calibration based on RNN with feature extraction

Hui Zhang, Yong Ni, Yue Chang

Proceedings Volume 7130, Fourth International Symposium on Precision Mechanical Measurements; 713019 (2008) https://doi.org/10.1117/12.819583
Event: Fourth International Symposium on Precision Mechanical Measurements, 2008, Anhui, China

Abstract

In this work a novel method is proposed for the sensor calibration, by combining Recurrent Neural Network (RNN) with feature extraction. RNN is used as the neural network model trained to calibrate the measuring error, while Kernel Independent Component Analysis (KICA) and Independent Component Analysis (ICA) are introduced in as the feature extraction as comparison. And by examining the data of an example of temperature sensor calibration of Agilent 34970, it is shown that the proposed methods can both perform good calibration comparing with single RBF method. And the KICA method performs better than the ICA method.

Citation Download Citation

Hui Zhang, Yong Ni, and Yue Chang "Modeling the sensor calibration based on RNN with feature extraction", Proc. SPIE 7130, Fourth International Symposium on Precision Mechanical Measurements, 713019 (31 December 2008); https://doi.org/10.1117/12.819583

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available