الفهرس 
LEAST MEAN SQUARE(LMS) ALGORITHMOnly 14 pages are availabe for public view
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Abstract
This letter describes the performance characteristics of the LMS adaptive filter, a digital filter composed of a
tapped delay line and adjustable weights, whose impulse
response is controlled by an adaptive a-lgorithm. For stationary
stochastic inputs, the mean-square error, the
difference between the filter output and externally ~upplied
input called the ”desired response”. is a quadratic function
of the weights.
In general, it can be shown that far stationary input
and sufficiently small adaptive step size, the speed of
convergence of the LMS algorithm i& dependent on the eigenvalue
spread or the ratio of maximum eigenvalues to minimum
eigenvalues (~max/~min), of the input data autocorrelation
.. tr1.x. This ratio, often referred to as the ”condition
number”, is a measure of ill-conditioning of the matrix.
Slow convergence rate can be expected when this ratio is
14rqe.
An approach to accelerate the convergence rate is to
somehow transform the input signal RXn” into another Signal
with corresponding autocorrelation matrix having smaller
eigenvalue spread. This can be Achived by performing the
adaptive filtering insome orthogonal transform domain.