AFT Linear Prediction VI

LabVIEW 2013 Adaptive Filter Toolkit Help

Edition Date: June 2013

Part Number: 372357B-01

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Performs adaptive linear prediction by estimating the autoregressive (AR) model of an input signal. Wire data to the signal input to determine the polymorphic instance to use or manually select the instance.

Example

Use the pull-down menu to select an instance of this VI.

 Select an instance AFT Linear Prediction (PtbyPt)AFT Linear Prediction (Waveform)AFT Linear Prediction (Array)

AFT Linear Prediction (PtbyPt)

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. You must specify a real-valued adaptive filter for this parameter. This VI returns errors if you wire a complex-valued adaptive filter to this parameter.
signal specifies the signal you want this VI to process.
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.
 Note  This parameter is valid only if the adaptive filter uses a least mean squares (LMS) algorithm. If the adaptive filter uses the normalized LMS or normalized leaky LMS algorithm, the value must be less than 2.
prediction error returns the prediction error e(n).
error out contains error information. This output provides standard error out functionality.
AR coefficients returns the coefficients of the autoregressive (AR) model this VI estimates.

AFT Linear Prediction (Waveform)

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. You must specify a real-valued adaptive filter for this parameter. This VI returns errors if you wire a complex-valued adaptive filter to this parameter.
signal specifies the signal you want this VI to process.
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.
 Note  This parameter is valid only if the adaptive filter uses a least mean squares (LMS) algorithm. If the adaptive filter uses the normalized LMS or normalized leaky LMS algorithm, the value must be less than 2.
prediction error returns the prediction error e(n).
error out contains error information. This output provides standard error out functionality.
AR coefficients returns the coefficients of the autoregressive (AR) model this VI estimates.

AFT Linear Prediction (Array)

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. You must specify a real-valued adaptive filter for this parameter. This VI returns errors if you wire a complex-valued adaptive filter to this parameter.
signal specifies the signal you want this VI to process.
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.
 Note  This parameter is valid only if the adaptive filter uses a least mean squares (LMS) algorithm. If the adaptive filter uses the normalized LMS or normalized leaky LMS algorithm, the value must be less than 2.
prediction error returns the prediction error e(n).
error out contains error information. This output provides standard error out functionality.
AR coefficients returns the coefficients of the autoregressive (AR) model this VI estimates.

Example

Refer to the Use Adaptive Filter for Linear Prediction VI in the labview\examples\Adaptive Filters\Getting Started directory for an example of using the AFT Linear Prediction VI.

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