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.
Original language | English |
---|---|
Article number | 6977884 |
Pages (from-to) | 953-957 |
Number of pages | 5 |
Journal | IEEE Signal Processing Letters |
Volume | 22 |
Issue number | 7 |
DOIs | |
Publication status | Published - 1 Jul 2015 |
Keywords
- Kernel adaptive filter
- KLMS
- recurrent fashion
- single feedback
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics