Cart (Loading....) | Create Account
Close category search window
 

Sensor monitoring using a fuzzy neural network with an automatic structure constructor

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

9 Author(s)
Man Gyun Na ; Dept. of Nucl. Eng., Chosun Univ., Kwangju, South Korea ; Young Rok Sim ; Kyung Ho Park ; Sun Mi Lee
more authors

The performance of fuzzy neural networks applied to sensor monitoring strongly depends on the selection of input signals. A large number of input signals may be involved to estimate an output signal for failure detection. However, as the number of input signals increases, the required training time increases exponentially and the uncertainty of the model increases significantly due to the irrelevant and/or the redundant inputs. In this paper, a fuzzy neural network with an optimal structure constructor has been successfully developed to achieve a reliable and efficient sensor monitoring system. A fuzzy neural network is used to estimate an output signal from the selected input signals. Correlation analysis and genetic algorithm (GA) are combined for automatic input selection. In addition, the optimal number of fuzzy rules is accomplished automatically by the GA integrated along with the automatic input selection. The status of sensor health is determined by applying sequential probability ratio test to the residuals between the measured signals and the estimated signals. The proposed sensor monitoring system has been validated by using a variety of sensor signals acquired from Yonggwang units 3 and 4 pressurized water reactors.

Published in:

Nuclear Science, IEEE Transactions on  (Volume:50 ,  Issue: 2 )

Date of Publication:

Apr 2003

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.