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• W2022669094 abstract "The kernel least-mean-square (KLMS) algorithm is a popular algorithm in nonlinear adaptive filtering due to its simplicity and robustness. In kernel adaptive filters, the statistics of the input to the linear filter depends on the parameters of the kernel employed. Moreover, practical implementations require a finite nonlinearity model order. A Gaussian KLMS has two design parameters, the step size and the Gaussian kernel bandwidth. Thus, its design requires analytical models for the algorithm behavior as a function of these two parameters. This paper studies the steady-state behavior and the transient behavior of the Gaussian KLMS algorithm for Gaussian inputs and a finite order nonlinearity model. In particular, we derive recursive expressions for the mean-weight-error vector and the mean-square-error. The model predictions show excellent agreement with Monte Carlo simulations in transient and steady state. This allows the explicit analytical determination of stability limits, and gives opportunity to choose the algorithm parameters a priori in order to achieve prescribed convergence speed and quality of the estimate. Design examples are presented which validate the theoretical analysis and illustrates its application." @default.
• W2022669094 created "2016-06-24" @default.
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• W2022669094 date "2012-05-01" @default.
• W2022669094 modified "2023-10-16" @default.
• W2022669094 title "Stochastic Behavior Analysis of the Gaussian Kernel Least-Mean-Square Algorithm" @default.
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• W2022669094 doi "https://doi.org/10.1109/tsp.2012.2186132" @default.
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