.png){kind=icon}
.png){kind=icon}
Return
Regularized Kernel Least Mean Square Algorithm with Multiple-delay Feedback
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2015.2503000.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In the design of adaptive filters, feedback can be utilized to improve the convergence rate and filtering accuracy. This letter introduces a feedback structure with multiple delay to design kernel adaptive filters. A regularized loss function is minimized by using the steepest descent method. The past information of output is therefore reused to update the filter weights in a recurrent fashion, resulting in a novel regularized kernel least mean square algorithm with multiple-delay feedback (RKLMS-MDF). Compared with other kernel adaptive filters with or without feedback, RKLMS-MDF can improve the filtering performance from the respects of the convergence rate and the steady-state mean square error.
Keywords:
Kernel adaptive filters
multiple-delay feedback
recurrent fashion
sparsification
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.6
Papers:
1.1W
Citations:
1.7W