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

Selecting features for nuclear transients classification by means of genetic algorithms

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

3 Author(s)
Zio, E. ; Dept. of Nucl. Eng., Politecnico di Milano, Milan, Italy ; Baraldi, P. ; Pedroni, N.

The issue of feature selection is particularly critical for the application of monitoring and "on condition" diagnostic techniques to complex plants, like the nuclear power plants, where hundreds of parameters are measured. Indeed, irrelevant and noisy features unnecessarily increase the complexity of the problem and can degrade the diagnostic performance. In this paper, the problem of choosing among the several measured plant parameters those to be used for efficient, early transient diagnosis is tackled by means of genetic algorithms. Three different schemes for simultaneously performing the feature selection and the training of an empirical diagnostic classifier are presented. The first approach employs a single objective genetic algorithm to search the vector of features optimal with respect to the classification performance of a Fuzzy K-Nearest Neighbors classifier. With reference to the same classifier, the second and third approaches embrace a multi-objective perspective to find feature sets that achieve high classification performances with low numbers of features. In all cases, validation of the performance of the classifiers based on the optimal feature subsets identified by the genetic algorithm is successively carried out with respect to transients never used during the feature selection phase. The effectiveness of the proposed approaches is tested on a diagnostic problem regarding the classification of simulated transients in the feedwater system of a Boiling Water Reactor.

Published in:

Nuclear Science, IEEE Transactions on  (Volume:53 ,  Issue: 3 )

Date of Publication:

June 2006

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.