Construct least mean square (LMS) adaptive algorithm object
alg = lms(stepsize)
alg = lms(stepsize,leakagefactor)
lms function creates an adaptive algorithm
object that you can use with the
dfe function to create an
equalizer object. You can then use the equalizer object with the
equalize function to equalize a signal.
To learn more about the process for equalizing a signal, see Adaptive Algorithms.
alg = lms(stepsize) constructs
an adaptive algorithm object based on the least mean square (LMS)
algorithm with a step size of
alg = lms(stepsize,leakagefactor) sets
the leakage factor of the LMS algorithm.
be between 0 and 1. A value of 1 corresponds to a conventional weight
update algorithm, and a value of 0 corresponds to a memoryless update
The table below describes the properties of the LMS adaptive algorithm object. To learn how to view or change the values of an adaptive algorithm object, see Access Properties of an Adaptive Algorithm.
|Fixed value, |
|LMS step size parameter, a nonnegative real number|
|LMS leakage factor, a real number between 0 and 1|
For examples that use this function, see Equalize Using a Training Sequence in MATLAB, Example: Equalizing Multiple Times, Varying the Mode, and Example: Adaptive Equalization Within a Loop.
Referring to the schematics presented in Adaptive Algorithms, define w as the vector of all weights wi and define u as the vector of all inputs ui. Based on the current set of weights, w, this adaptive algorithm creates the new set of weights given by
LeakageFactor) w + (
where the * operator denotes the complex conjugate.
 Farhang-Boroujeny, B., Adaptive Filters: Theory and Applications, Chichester, England, John Wiley & Sons, 1998.
 Haykin, Simon, Adaptive Filter Theory, Third Ed., Upper Saddle River, NJ, Prentice-Hall, 1996.
 Kurzweil, Jack, An Introduction to Digital Communications, New York, John Wiley & Sons, 2000.
 Proakis, John G., Digital Communications, Fourth Ed., New York, McGraw-Hill, 2001.