Referenced Items are not available for this document.
The class of least mean square (LMS) algorithms employing a general error nonlinearity is considered. A linearization approach is used to characterize the convergence and performance of this class of algorithms for an independent and identically distributed (IID) input. The analysis results are entirely consistent with those of the LMS algorithm and several of its variants. The results also encompass those of a previous work that considered the same class of algorithms for arbitrary and Gaussian inputs.
Published in:
Signals, Systems & Computers, 1998. Conference Record of the Thirty-Second Asilomar Conference on
(Volume:1
)
Date of Conference: 1-4 Nov. 1998
- Page(s):
- 556 - 559 vol.1
- Meeting Date :
- 01 Nov 1998-04 Nov 1998
- ISSN :
- 1058-6393
- Print ISBN:
- 0-7803-5148-7
- INSPEC Accession Number:
- 6314194
- Conference Location :
- Pacific Grove, CA, USA
- Digital Object Identifier :
- 10.1109/ACSSC.1998.750925
- Product Type:
- Conference Publications
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 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
