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Filtered XLMS filter

The `dsp.FilteredXLMSFilter`

System object™ computes output, error and coefficients using filtered-x least mean square FIR
adaptive filter.

To implement the adaptive FIR filter object:

Create the

`dsp.FilteredXLMSFilter`

object and set its properties.Call the object with arguments, as if it were a function.

To learn more about how System objects work, see What Are System Objects?

returns a
filtered-x least mean square FIR adaptive filter System object, `fxlms`

= dsp.FilteredXLMSFilter`fxlms`

. This System object is used to compute the filtered output and the filter error for a given
input and desired signal.

returns a `fxlms`

= dsp.FilteredXLMSFilter(`len`

)`FilteredXLMSFilter`

System object, `fxlms`

, with the `Length`

property
set to `len`

.

returns a `fxlms`

= dsp.FilteredXLMSFilter(`Name,Value`

)`FilteredXLMSFilter`

System object, `fxlms`

, with each specified property set to the
specified value. Enclose each property name in single quotes. Unspecified properties have
default values.

`[`

filters the input `y`

,`err`

] = fxlms(`x`

,`d`

)`x`

, using `d`

as the desired
signal, and returns the filtered output `y`

and the filter error
`err`

. The System object estimates the filter weights needed to minimize the error
between the output signal and the desired signal. You can access these coefficients by
accessing the `Coefficients`

property of the object. This can be done
only after calling the object. For example, to access the optimized coefficients of the
`fxlms`

filter, call `fxlms.Coefficients`

after you
pass the input and desired signal to the object.

To use an object function, specify the
System object as the first input argument. For
example, to release system resources of a System object named `obj`

, use
this syntax:

release(obj)

[1] Kuo, S.M. and Morgan, D.R. *Active
Noise Control Systems: Algorithms and DSP Implementations*.
New York: John Wiley & Sons, 1996.

[2] Widrow, B. and Stearns, S.D. *Adaptive
Signal Processing*. Upper Saddle River, N.J: Prentice Hall,
1985.