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

Combination of radial basis function (RBF) and time delayed neural networks (TDNN) for fault diagnosis of automobile transmission gears using general parameter learning and adaptation

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)
Satoh, S. ; Dept. of Comput. Sci. & Syst. Eng., Muroran Inst. of Technol., Japan ; Yakuwa, F. ; Dote, Y.

By taking advantages of fuzzy systems and neural networks, a fast and accurate Sugeno's type-I fuzzy system (Type-I fuzzy system) is implemented with the combination of the Gaussian radial basis function network (GP-RBFN) and the time delayed neural network (TDNN), which is based on local modeling using fast general parameter (GP) learning and adaptive algorithms. The proposed GP algorithm applied to adaptation and learning for neural networks is very suitable to parameter optimization of such local linear models in blended multiple model structures. It is applied to a fault detection application. It is experimentally confirmed that the developed fuzzy neural network is more accurate and faster than the RBFN.

Published in:

Systems, Man and Cybernetics, 2003. IEEE International Conference on  (Volume:2 )

Date of Conference:

5-8 Oct. 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.