Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25035
Title: Kernel least mean square with single feedback
Authors: Zhao, J
Liao, X
Wang, S
Tse, CK 
Keywords: Kernel adaptive filter
KLMS
Recurrent fashion
Single feedback
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE Signal processing letters, 2014, v. 22, no. 7, 6977884, p. 953-957 How to cite?
Journal: IEEE signal processing letters 
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.
URI: http://hdl.handle.net/10397/25035
ISSN: 1070-9908
EISSN: 1558-2361
DOI: 10.1109/LSP.2014.2377726
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