Construct signed least mean square (LMS) adaptive algorithm object

`alg = signlms(stepsize)`

alg = signlms(stepsize,* algtype*)

The `signlms`

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 = signlms(stepsize)`

constructs
an adaptive algorithm object based on the signed least mean square
(LMS) algorithm with a step size of `stepsize`

.

`alg = signlms(stepsize,`

constructs
an adaptive algorithm object of type * algtype*)

`algtype`

`algtype`

Value
of `algtype` | Type of Signed LMS Algorithm |
---|---|

`'Sign LMS'` | Sign LMS (default) |

```
'Signed Regressor
LMS'
``` | Signed regressor LMS |

`'Sign Sign LMS'` | Sign-sign LMS |

The table below describes the properties of the signed 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` | Type of signed LMS algorithm,
corresponding to the input argument.
You cannot change the value of this property after creating the object.`algtype` |

`StepSize` | LMS step size parameter, a nonnegative real number |

`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. |

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

[2] Kurzweil, J., *An Introduction
to Digital Communications*, New York, John Wiley &
Sons, 2000.

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