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

The classification of traffic sign base on fuzzy characteristics training set

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

1 Author(s)
Shuangdong Zhu ; Fac. of Inf. Sci. & Technol., Ningbo Univ., China

The BP networks have the ability of nonlinear mapping to realize the function of N categoriser. However, BP networks need to be trained again when the training set is changed. Meanwhile, the larger the network is, the slower convergence rate is, and the poorer result of classification and recognition is. So, two-hierarchy neural network classifier for recognition of traffic signs is presented, the first hierarchy classification which consists of a single BP network is used to coarsely classify indicative signs, warning signs and prohibitive signs. Classify the sample of similar characteristic and superpose the image pixel of each type, then a new sample called fuzzy characteristic training set is obtained. The correctness of classification is up to 100% for testing set with white Gaussian noise, while the first hierarchy neural network is trained. Taking fuzzy characteristic training as the first classification in the two-hierarchy neural network not only reduces the scale of training set but also makes the neural network fault tolerant. The convergence rate and classification also improve. Moreover it indicates that this kind of training method is similar to thinking models of biological intelligence.

Published in:

Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on  (Volume:6 )

Date of Conference:

15-19 June 2004

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.