Construct variable-step-size least mean square (LMS) adaptive algorithm object

`alg = varlms(initstep,incstep,minstep,maxstep)`

The `varlms`

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 = varlms(initstep,incstep,minstep,maxstep)`

constructs
an adaptive algorithm object based on the variable-step-size least
mean square (LMS) algorithm. `initstep`

is the initial
value of the step size parameter. `incstep`

is the
increment by which the step size changes from iteration to iteration. `minstep`

and `maxstep`

are
the limits between which the step size can vary.

The table below describes the properties of the variable-step-size 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.

Property | Description |
---|---|

`AlgType` | Fixed value, ```
'Variable
Step Size LMS'
``` |

`LeakageFactor` | LMS leakage factor, a real number between 0 and 1. A value of 1 corresponds to a conventional weight update algorithm, while a value of 0 corresponds to a memoryless update algorithm. |

`InitStep` | Initial value of step size when the algorithm starts |

`IncStep` | Increment by which the step size changes from iteration to iteration |

`MinStep` | Minimum value of step size |

`MaxStep` | Maximum value of step size |

Also, when you use this adaptive algorithm object to create
an equalizer object (via the `lineareq`

or `dfe`

function), the equalizer object has
a `StepSize`

property. The property value is a vector
that lists the current step size for each weight in the equalizer.

For an example that uses this function, see Linked Properties of an Equalizer Object.

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

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