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lms

Construct least mean square (LMS) adaptive algorithm object

Syntax

alg = lms(stepsize)
alg = lms(stepsize,leakagefactor)

Description

The lms function creates an adaptive algorithm object that you can use with the lineareq function or 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 stepsize.

alg = lms(stepsize,leakagefactor) sets the leakage factor of the LMS algorithm. leakagefactor must 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 algorithm.

Properties

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.

PropertyDescription
AlgTypeFixed value, 'LMS'
StepSizeLMS step size parameter, a nonnegative real number
LeakageFactorLMS leakage factor, a real number between 0 and 1

More About

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Algorithms

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 + (StepSize) u*e

where the * operator denotes the complex conjugate.

References

[1] Farhang-Boroujeny, B., Adaptive Filters: Theory and Applications, Chichester, England, John Wiley & Sons, 1998.

[2] Haykin, Simon, Adaptive Filter Theory, Third Ed., Upper Saddle River, NJ, Prentice-Hall, 1996.

[3] Kurzweil, Jack, An Introduction to Digital Communications, New York, John Wiley & Sons, 2000.

[4] Proakis, John G., Digital Communications, Fourth Ed., New York, McGraw-Hill, 2001.

See Also

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Introduced before R2006a

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