Approach for the evaluation of indirect measurement uncertainty based on neural networks

Jianmin Zhu; Zhongyu Wang; Xintao Xia; Ping Li

doi:10.1117/12.521825

2 September 2003 Approach for the evaluation of indirect measurement uncertainty based on neural networks

Jianmin Zhu, Zhongyu Wang, Xintao Xia, Ping Li

Proceedings Volume 5253, Fifth International Symposium on Instrumentation and Control Technology; (2003) https://doi.org/10.1117/12.521825
Event: Fifth International Symposium on Instrumentation and Control Technology, 2003, Beijing, China

Abstract

A new approach, in which a nonparametric measurement model is built on the radial basis functional neural networks to evaluate the indirect measurement uncertainty, is proposed in this paper to solve the difficult problem of evaluating the uncertainty of indirect measurement with no measurement model. By determining the center of basis functions based on the clustering result of training samples, neural networks can still be secured a high model building accuracy even when there are relatively fewer training samples. By using the measurement model built to approximately compute the sensitivity coefficient that reflects the uncertainty propagating law of each influence quantity, it is possible to evaluate the result of indirect measurement and its uncertainties. As is demonstrated in simulation results, the method of indirect measurement model building based on neural networks requires no prior knowledge of the measuring process, enjoys a relatively higher modeling accuracy, effectively secures the high accuracy in evaluating the indirect measurement uncertainty and can serve as a beneficial complement to Guide to the Expression of Uncertainty in Measurement.

Citation Download Citation

Jianmin Zhu, Zhongyu Wang, Xintao Xia, and Ping Li "Approach for the evaluation of indirect measurement uncertainty based on neural networks", Proc. SPIE 5253, Fifth International Symposium on Instrumentation and Control Technology, (2 September 2003); https://doi.org/10.1117/12.521825

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