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

Enhanced robustness of multilayer perceptron training

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)
Delashmit, W.H. ; Lockheed Martin Missiles & Fire Control, Dallas, TX, USA ; Manry, M.T.

Due to the chaotic nature of multilayer perceptron training, training error usually fails to be a monotonically non-increasing function of the number of hidden units. An initialization and training methodology is developed to significantly increase the probability that the training error is monotonically non-increasing. First a structured initialization generates the random weights in a particular order. Second, larger networks are initialized using weights from smaller trained networks. Lastly, the required number of iterations is calculated as a function of network size.

Published in:

Signals, Systems and Computers, 2002. Conference Record of the Thirty-Sixth Asilomar Conference on  (Volume:2 )

Date of Conference:

3-6 Nov. 2002

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.