Referenced Items are not available for this document.
This paper considers series-cascade nonlinear adaptive filter architectures consisting of a linear input filter, a memoryless polynomial nonlinearity, and a linear output filter (LNL). The learning characteristics of the LNL structure are studied in terms of performance and complexity. Replacing the linear input stage and the memoryless nonlinear stage of the LNL model with a Volterra module is then considered. Adaptive algorithms are summarized for these structures and experimental examples are used to illustrate performance for the identification of an acoustic echo channel.
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
- Page(s):
- 1015 - 1018 vol.2
- ISSN :
- 1058-6393
- Print ISBN:
- 0-7803-7576-9
- INSPEC Accession Number:
- 7755596
- Conference Location :
- Pacific Grove, CA, USA
- Digital Object Identifier :
- 10.1109/ACSSC.2002.1196937
- 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.
