Compute output, error, and weights using block LMS adaptive algorithm

The `dsp.BlockLMSFilter`

System
object™ computes output, error, and weights using the block LMS adaptive
algorithm.

To compute the output, error, and weights:

Create the

`dsp.BlockLMSFilter`

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? (MATLAB).

returns an
adaptive FIR filter, `blms`

= dsp.BlockLMSFilter`blms`

, that filters the input signal and computes
filter weights based on the block least mean squares (LMS) algorithm.

returns an adaptive FIR filter, `blms`

= dsp.BlockLMSFilter(`length`

,`blocksize`

)`blms`

, with the
`Length`

property set to `length`

and the
`BlockSize`

property set to `blocksize`

.

returns an adaptive FIR filter, `blms`

= dsp.BlockLMSFilter(`Name,Value`

)`blms`

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

`[`

filters input `y`

,`err`

,`wts`

] = blms(`x`

,`d`

,`mu`

,`a`

,`r`

)`x`

, using `d`

as the desired signal,
`mu`

as the step size, `a`

as the adaptation
control, and `r`

as the reset signal. The object returns the filtered
output `y`

, the filter error `err`

, and the adapted
filter weights `wts`

. Set the properties appropriately to provide all
possible inputs.

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)

This object implements the algorithm, inputs, and outputs described on the Block LMS Filter block reference page. The object properties correspond to the block parameters.