Referenced Items are not available for this document.
We present a method for optimizing adaptation laws that are generalizations of the LMS algorithm. Time-varying parameters of linear regression models are estimated in situations where the regressors are stationary or have slowly time-varying properties. The parameter variations are modeled as ARIMA processes and the aim is to use such prior information to provide high-performance filtering, prediction or fixed lag smoothing estimates for arbitrary lags. The method is based on a novel signal transformation that recasts the algorithm design problem into a Wiener design
Published in:
Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on
(Volume:6
)
Date of Conference: 2001
- Page(s):
- 3861 - 3864 vol.6
- Meeting Date :
- 07 May 2001-11 May 2001
- ISSN :
- 1520-6149
- Print ISBN:
- 0-7803-7041-4
- INSPEC Accession Number:
- 7144966
- Conference Location :
- Salt Lake City, UT
- Digital Object Identifier :
- 10.1109/ICASSP.2001.940686
- 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.
