2020
DOI: 10.1145/3376924
|View full text |Cite
|
Sign up to set email alerts
|

Kernel Normalised Least Mean Squares with Delayed Model Adaptation

Abstract: Kernel adaptive filters (KAFs) are non-linear filters which can adapt temporally and have the additional benefit of being computationally efficient through use of the “kernel trick”. In a number of real-world applications, such as channel equalisation, the non-linear mapping provides significant improvements over conventional linear techniques such as the least mean squares (LMS) and recursive least squares (RLS) algorithms. Prior works have focused mainly on the theory and accuracy of KAFs, with little resear… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Publication Types

Select...

Relationship

0
0

Authors

Journals

citations
Cited by 0 publications
references
References 23 publications
0
0
0
Order By: Relevance

No citations

Set email alert for when this publication receives citations?