Package: dsp
Compute output, error, and weights using Block LMS adaptive algorithm
The BlockLMSFilter
object computes output,
error, and weights using the Block LMS adaptive algorithm.
To compute the output, error, and weights:
Define and set up your adaptive FIR filter. See Construction.
Call step
to compute the output,
error, and weights according to the properties of dsp.BlockLMSFilter
.
The behavior of step
is specific to each object in
the toolbox.
Note:
Starting in R2016b, instead of using the 
H=dsp.BlockLMSFilter
returns
an adaptive FIR filter, H
, that filters the input
signal and computes filter weights based on the Block Least Mean Squares
(LMS) algorithm.
H=dsp.BlockLMSFilter('
returns
an adaptive FIR filter, PropertyName
',PropertyValue
,...)H
, with each specified
property set to the specified value.
H=dsp.BlockLMSFilter(length,blocksize,'
returns
an adaptive FIR filter, PropertyName
',...PropertyValue
,...)H
, with the Length
property
set to length
, the BlockSize
property
set to blocksize
, and other specified properties
set to the specified values.

Length of FIR filter weights vector Specify the length of the FIR filter weights vector as a positive
integer scalar. The default is 

Number of samples acquired before weight adaptation Specify the number of samples of the input signal to acquire
before the object updates the filter weights. The input frame length
must be an integer multiple of the block size. The default is 

Source of adaptation step size Choose to specify the adaptation step size factor as 

Adaptation step size Specify the adaptation step size factor as a scalar, nonnegative
numeric value. The default is 

Leakage factor used in Leaky LMS algorithm Specify the leakage factor used in Leaky LMS algorithm as a
scalar numeric value between 

Initial values of filter weights Specify the initial values of the filter weights as a scalar
or a vector of length equal to the 

Additional input to enable adaptation of filter weights. Specify when the object should adapt the filter weights. By
default, the value of this property is 

Additional input to enable weights reset Specify whether the FIR filter can reset the filter weights.
By default, the value of this property is 

Condition that triggers the resetting of filter weights Specify the event to reset the filter weights as one of 

Output filter weights Set this property to 
clone  Create adaptive block LMS filter object with same property values 
getNumInputs  Number of expected inputs to step method 
getNumOutputs  Number of outputs of step method 
isLocked  Locked status for input attributes and nontunable properties 
msesim  Meansquare error for Block LMS filter 
release  Allow property value and input characteristics changes 
reset  Reset internal states of adaptive FIR filter object 
step  Filter inputs using Block LMS algorithm 
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.