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

A Numerical Prediction Product FNN Prediction Model Based on Condition Number and Analog Deviation

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

2 Author(s)
Long Jin ; Guangxi Res. Inst. of Meteorological Disasters Mitigation, Nanning ; Xvming Shi

Aiming at the problem that the fuzzy neural network (FNN) technique itself does not provide the input matrix to the FNN prediction model, we present a prediction modeling methodology which combines the computation and analysis of condition number with FNN, and design the computation and analysis of analog deviation for the input matrix to choose samples close correlated with predictand as training samples, thus effectively reducing the scale of network and evidently enhancing the prediction ability of the FNN prediction model. Using the same CMA T213 and Japanese numerical prediction product (NPP) data, we performed the contrast experiments and analyses of the FNN prediction model for daily regional mean precipitation based on condition number and analog deviation against the condition number-FNN prediction model and the traditional stepwise regression prediction model, and results show that under the condition of the same number of selected predictors, the prediction accuracy of the FNN prediction model based on condition number and analog deviation is 12.6% higher than that of the stepwise regression model in the experiment of independent samples of 49 days.

Published in:

Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on  (Volume:2 )

Date of Conference:

July 30 2007-Aug. 1 2007

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.