Kernel Least Mean Square with Single Feedback
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Article number | 6977884 |
| Pages (from-to) | 953-957 |
| Journal / Publication | IEEE Signal Processing Letters |
| Volume | 22 |
| Issue number | 7 |
| Online published | 5 Dec 2014 |
| Publication status | Published - Jul 2015 |
| Externally published | Yes |
Link(s)
Abstract
In this letter, a novel kernel adaptive filtering algorithm, namely the kernel least mean square with single feedback (SF-KLMS) algorithm, is proposed. In SF-KLMS, only a single delayed output is used to update the weights in a recurrent fashion. The use of past information accelerates the convergence rate significantly. Compared with the kernel adaptive filter using multiple feedback, SF-KLMS has a more compact and efficient structure. Simulations in the context of time-series prediction and nonlinear regression show that SF-KLMS outperforms not only the kernel adaptive filter with multiple feedback but also the kernel adaptive filter without feedback in terms of convergence rate and mean square error.
Research Area(s)
- Kernel adaptive filter, KLMS, recurrent fashion, single feedback
Citation Format(s)
Kernel Least Mean Square with Single Feedback. / Zhao, Ji; Liao, Xiaofeng; Wang, Shiyuan et al.
In: IEEE Signal Processing Letters, Vol. 22, No. 7, 6977884, 07.2015, p. 953-957.
In: IEEE Signal Processing Letters, Vol. 22, No. 7, 6977884, 07.2015, p. 953-957.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
