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ICASSP
2011
IEEE

Stochastic behavior analysis of the Gaussian Kernel Least Mean Square algorithm

11 years 3 months ago
Stochastic behavior analysis of the Gaussian Kernel Least Mean Square algorithm
Like its linear counterpart, the Kernel Least Mean Square (KLMS) algorithm is also becoming popular in nonlinear adaptive filtering due to its simplicity and robustness. The “kernelization” of the linear adaptive filters modifies the statistics of the input signals, which now depends on the parameters of the used kernel. A Gaussian KLMS has two design parameters; the step size and the kernel bandwidth. Thus, new analytical models are required to predict the kernel-based algorithm behavior as a function of the design parameters. This paper studies the stochastic behavior of the Gaussian KLMS algorithm for white Gaussian input signals. The resulting model accurately predicts the algorithm behavior and can be used for choosing the algorithm parameters in order to achieve a prescribed performance.
Wemerson D. Parreira, José Carlos M. Bermud
Added 20 Aug 2011
Updated 20 Aug 2011
Type Journal
Year 2011
Where ICASSP
Authors Wemerson D. Parreira, José Carlos M. Bermudez, Cédric Richard, Jean-Yves Tourneret
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