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

A Vectorization-Optimization-Method-Based Type-2 Fuzzy Neural Network for Noisy Data Classification

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)
Gin-Der Wu ; Dept. of Electr. Eng., Nat. Chi Nan Univ., Nantou, Taiwan ; Pang-Hsuan Huang

This paper proposes a vectorization-optimization-method (VOM)-based type-2 fuzzy neural network (VOM2FNN) for noisy data classification. In handling problems with uncertainties, such as noisy data, type-2 fuzzy systems usually outperform their type-1 counterparts. Hence, type-2 fuzzy sets are adopted in the antecedent parts to model the uncertainty. To consider the classification problems, the discriminative capability is crucial to determine the performance. Therefore, a VOM is proposed in the consequent parts to increase the discriminability and reduce the parameters. Compared with other existing fuzzy neural networks, the novelty of the proposed VOM2FNN is its consideration of both uncertainty and discriminability. The effectiveness of the proposed VOM2FNN is demonstrated by three classification problems. Experimental results and theoretical analysis indicate that the proposed VOM2FNN performs better than the other fuzzy neural networks.

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:21 ,  Issue: 1 )

Date of Publication:

Feb. 2013

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.