Construct normalized least mean square (LMS) adaptive algorithm object

`alg = normlms(stepsize)`

alg = normlms(stepsize,bias)

The `normlms`

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 = normlms(stepsize)`

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

and
a bias parameter of zero.

`alg = normlms(stepsize,bias)`

sets
the bias parameter of the normalized LMS algorithm. `bias`

must
be between 0 and 1. The algorithm uses the bias parameter to overcome
difficulties when the algorithm's input signal is small.

The table below describes the properties of the normalized 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, ```
'Normalized
LMS'
``` |

`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. |

`Bias` | Normalized LMS bias parameter, a nonnegative real number |

For an example that uses this function, see Delays from Equalization.

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

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