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 filtering, the statistics of the input
to the linear filter depends on the kernel and its parameters. Moreover,
practical implementations on systems estimation require a
finite non-linearity model order. In order to obtain finite order
models, many kernelized adaptive filters use a dictionary of kernel
functions. Dictionary size also depends on the kernel and its
parameters. Therefore, KLMS may have different performances
on the estimation of a nonlinear system, the time of convergence,
and the accuracy using a different kernel. In order to analyze the
performance of KLMS with different kernels, this paper proposes
the use of the Monte Carlo simulation of both steady-state and the
transient behavior of the KLMS algorithm using different types of
kernel functions and Gaussian inputs.