Thesis
Nonlinear adaptive digital filter with sequential regression (SER) algorithm
University of Iowa
Master of Science (MS), University of Iowa
Autumn 1984
DOI: 10.25820/etd.008499
Abstract
A class of adaptive nonlinear filtering algorithms are discussed in this thesis. The algorithms derived in this thesis use the sequential regression (SER) method to compute the matrix inverse in a recursive way. Assuming Gaussian signals, the algorithms update the second order Volterra filter coefficients. Among those algorithms, long-term equally weighted SER algorithm is suitable for adaptive filtering applications related to stationary signals and the short-term exponentially weighted SER algorithm is suitable for those in nonstationary environments. Computer simulation results pertaining to SER algorithm are presented. For the purpose of comparison, corresponding result using least mean square (LMS) algorithm is also included.
Details
- Title: Subtitle
- Nonlinear adaptive digital filter with sequential regression (SER) algorithm
- Creators
- Kuo-Kan Yu - University of Iowa
- Contributors
- Dae Hee Youn (Advisor) - University of Iowa
- Resource Type
- Thesis
- Degree Awarded
- Master of Science (MS), University of Iowa
- Degree in
- Electrical and Computer Engineering
- Date degree season
- Autumn 1984
- DOI
- 10.25820/etd.008499
- Publisher
- University of Iowa
- Number of pages
- vi, 70 pages
- Copyright
- No known copyright restrictions
- Comment
This thesis has been optimized for improved web viewing. If you require the original version, contact the University Archives at the University of Iowa: https://www.lib.uiowa.edu/sc/contact/
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9985224418802771
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