By Topic

A novel approach to the convergence of neural networks for signal processing

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)
Liu, R. ; Dept. of Electr. Eng., Notre Dame Univ., IN, USA ; Yih-Fang Huang ; Xie-Ting Ling

A novel deterministic approach to the convergence analysis of (stochastic) learning algorithms is presented. The link between the two is the new concept of time-average invariance, which is a property of deterministic signals but resembles that of stochastic signals which are ergodic and stationary

Published in:

Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on  (Volume:42 ,  Issue: 3 )