Download Help (Windows Only) 
Owning Palette: Adaptive Filters VIs
Requires: Adaptive Filter Toolkit
Filters an input signal with an adaptive filter and updates the coefficients of the adaptive filter iteratively. You must use the least mean squares (LMS) or recursive least squares (RLS) algorithms to create the adaptive filter. Wire data to the x(n) and d(n) inputs to determine the polymorphic instance to use or manually select the instance. The data type of the adaptive filter that you wire to the adaptive filter in input must match the instance of the AFT Filter Signal and Update Coefficients VI.
Note If you use the AFT Create FIR Fast Block LMS VI to create an adaptive filter, you can filter the input signal only by arrays or waveforms. 
Use the pulldown menu to select an instance of this VI.
reset? specifies whether to reset the internal states and coefficients of the adaptive filter to zeroes. The default is FALSE.  
adaptive filter in specifies the adaptive filter that you create.  
x(n) specifies the input signal x(n) to the adaptive filter.  
d(n) specifies the input signal d(n) to the adaptive filter.  
error in describes error conditions that occur before this node runs. This input provides standard error in functionality.  
step size specifies the step size for the adaptive filter. If this value is less than zero, this VI ignores this parameter and uses the step size that you specify when you create the adaptive filter. If this value is zero, this VI processes the input signal without updating the coefficients of the adaptive filter. If this value is greater than zero, this VI uses this value as the step size to update the coefficients of the adaptive filter. The default is –1.


adaptive filter out returns the adaptive filter in unchanged.  
y(n) returns the output signal from the adaptive filter.  
e(n) returns the error signal, which is the difference between d(n) and y(n).  
error out contains error information. This output provides standard error out functionality. 
reset? specifies whether to reset the internal states and coefficients of the adaptive filter to zeroes. The default is FALSE.  
adaptive filter in specifies the adaptive filter that you create.  
x(n) specifies the input signal x(n) to the adaptive filter. If the input adaptive filter uses the fast block least mean squares (LMS) algorithm, the number of samples in the x(n) waveform must equal the length of the adaptive filter.  
d(n) specifies the input signal d(n) to the adaptive filter. If the input adaptive filter uses the fast block least mean squares (LMS) algorithm, the number of samples in the d(n) waveform must equal the length of the adaptive filter.  
error in describes error conditions that occur before this node runs. This input provides standard error in functionality.  
step size specifies the step size for the adaptive filter. If this value is less than zero, this VI ignores this parameter and uses the step size that you specify when you create the adaptive filter. If this value is zero, this VI processes the input signal without updating the coefficients of the adaptive filter. If this value is greater than zero, this VI uses this value as the step size to update the coefficients of the adaptive filter. The default is –1.


adaptive filter out returns the adaptive filter in unchanged.  
y(n) returns the output signal from the adaptive filter.  
e(n) returns the error signal, which is the difference between d(n) and y(n).  
error out contains error information. This output provides standard error out functionality. 
reset? specifies whether to reset the internal states and coefficients of the adaptive filter to zeroes. The default is FALSE.  
adaptive filter in specifies the adaptive filter that you create.  
x(n) specifies the input signal x(n) to the adaptive filter. If the adaptive filter uses the fast block least mean squares (LMS) algorithm, the length of the x(n) array must equal the length of the adaptive filter.  
d(n) specifies the input signal d(n) to the adaptive filter. If the adaptive filter uses the fast block least mean squares (LMS) algorithm, the length of the d(n) array must equal the length of the adaptive filter.  
error in describes error conditions that occur before this node runs. This input provides standard error in functionality.  
step size specifies the step size for the adaptive filter. If this value is less than zero, this VI ignores this parameter and uses the step size that you specify when you create the adaptive filter. If this value is zero, this VI processes the input signal without updating the coefficients of the adaptive filter. If this value is greater than zero, this VI uses this value as the step size to update the coefficients of the adaptive filter. The default is –1.


adaptive filter out returns the adaptive filter in unchanged.  
y(n) returns the output signal from the adaptive filter.  
e(n) returns the error signal, which is the difference between d(n) and y(n).  
error out contains error information. This output provides standard error out functionality. 
reset? specifies whether to reset the internal states and coefficients of the adaptive filter to zeroes. The default is FALSE.  
adaptive filter in specifies the adaptive filter that you create.  
x(n) specifies the input signal x(n) to the adaptive filter.  
d(n) specifies the input signal d(n) to the adaptive filter.  
error in describes error conditions that occur before this node runs. This input provides standard error in functionality.  
step size specifies the step size for the adaptive filter. If this value is less than zero, this VI ignores this parameter and uses the step size that you specify when you create the adaptive filter. If this value is zero, this VI processes the input signal without updating the coefficients of the adaptive filter. If this value is greater than zero, this VI uses this value as the step size to update the coefficients of the adaptive filter. The default is –1.


adaptive filter out returns the adaptive filter in unchanged.  
y(n) returns the output signal from the adaptive filter.  
e(n) returns the error signal, which is the difference between d(n) and y(n).  
error out contains error information. This output provides standard error out functionality. 
reset? specifies whether to reset the internal states and coefficients of the adaptive filter to zeroes. The default is FALSE.  
adaptive filter in specifies the adaptive filter that you create.  
x(n) specifies the input signal x(n) to the adaptive filter. If the input adaptive filter uses the fast block least mean squares (LMS) algorithm, the number of samples in the x(n) waveform must equal the length of the adaptive filter.  
d(n) specifies the input signal d(n) to the adaptive filter. If the input adaptive filter uses the fast block least mean squares (LMS) algorithm, the number of samples in the d(n) waveform must equal the length of the adaptive filter.  
error in describes error conditions that occur before this node runs. This input provides standard error in functionality.  
step size specifies the step size for the adaptive filter. If this value is less than zero, this VI ignores this parameter and uses the step size that you specify when you create the adaptive filter. If this value is zero, this VI processes the input signal without updating the coefficients of the adaptive filter. If this value is greater than zero, this VI uses this value as the step size to update the coefficients of the adaptive filter. The default is –1.


adaptive filter out returns the adaptive filter in unchanged.  
y(n) returns the output signal from the adaptive filter.  
e(n) returns the error signal, which is the difference between d(n) and y(n).  
error out contains error information. This output provides standard error out functionality. 
reset? specifies whether to reset the internal states and coefficients of the adaptive filter to zeroes. The default is FALSE.  
adaptive filter in specifies the adaptive filter that you create.  
x(n) specifies the input signal x(n) to the adaptive filter. If the adaptive filter uses the fast block least mean squares (LMS) algorithm, the length of the x(n) array must equal the length of the adaptive filter.  
d(n) specifies the input signal d(n) to the adaptive filter. If the adaptive filter uses the fast block least mean squares (LMS) algorithm, the length of the d(n) array must equal the length of the adaptive filter.  
error in describes error conditions that occur before this node runs. This input provides standard error in functionality.  
step size specifies the step size for the adaptive filter. If this value is less than zero, this VI ignores this parameter and uses the step size that you specify when you create the adaptive filter. If this value is zero, this VI processes the input signal without updating the coefficients of the adaptive filter. If this value is greater than zero, this VI uses this value as the step size to update the coefficients of the adaptive filter. The default is –1.


adaptive filter out returns the adaptive filter in unchanged.  
y(n) returns the output signal from the adaptive filter.  
e(n) returns the error signal, which is the difference between d(n) and y(n).  
error out contains error information. This output provides standard error out functionality. 
Refer to the following VIs for examples of using the AFT Filter Signal and Update Coefficients VI:
Helpful
Not Helpful