# Documentation

### This is machine translation

Translated by
Mouseover text to see original. Click the button below to return to the English verison of the page.

Note: This page has been translated by MathWorks. Please click here
To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

# dsp.FastTransversalFilter System object

Fast Transversal filter

## Description

The dsp.FastTransversalFilter computes output, error and coefficients using a fast transversal least-squares FIR adaptive filter.

To implement the adaptive FIR filter object:

1. Define and set up your adaptive FIR filter object. See Construction.

2. Call step to implement the filter according to the properties of dsp.FastTransversalFilter. The behavior of step is specific to each object in the toolbox.

### Note

Starting in R2016b, instead of using the step method to perform the operation defined by the System object™, you can call the object with arguments, as if it were a function. For example, y = step(obj,x) and y = obj(x) perform equivalent operations.

## Construction

ftf = dsp.FastTransversalFilter returns a System object, ftf, which is a fast transversal, least-squares FIR adaptive filter. This System object is used to compute the filtered output and the filter error for a given input and desired signal.

ftf = dsp.FastTransversalFilter('PropertyName', PropertyValue,...) returns a FastTransversalFilter System object, ftf, with each specified property set to the specified value.

ftf = dsp.FastTransversalFilter(LEN,'PropertyName',PropertyValue,...) returns a FastTrasversalFilter System object, ftf. In this case, the Length property set to LEN, and other specified properties set to the specified values.

## Properties

 Method Method to calculate filter coefficients Specify the method used to calculate filter coefficients as one of 'Fast transversal least-squares' |'Sliding-window fast transversal least-squares'. The default value is 'Fast transversal least-squares'. For algorithms used to implement these three different methods, refer to [1]. This property is nontunable. Length Length of filter coefficients vectorSpecify the length of the FIR filter coefficients vector as a positive integer value. This property is nontunable. The default value is 32. SlidingWindowBlockLength Width of sliding window Specify the width of the sliding window as a positive integer value greater than or equal to the Length property value. This property applies only if the Method property is set to 'Sliding-window fast transversal least-squares'. The default vale is the value of the Length property. This property is nontunable. ForgettingFactor Fast transversal filter forgetting factor Specify the fast transversal filter forgetting factor as a positive numeric value. Setting this value to 1 denotes infinite memory while adaptation. Setting this property value to 1 denotes infinite memory while adapting to find the new filter. For best results, set this property to a value that lies in the range [1-0.5/L, 1], where L is the Length property value. This property applies only if the Method property is set to 'Fast transversal least-squares'. The default value is 1. This property is tunable. InitialPredictionErrorPower Initial prediction error power Specify the initial value of the forward and backward prediction error vectors as a positive numeric scalar. This scalar should be sufficiently large to maintain stability and prevent an excessive number of Kalman gain rescues. The default value is 10. This property is tunable. InitialConversionFactor Initial conversion factor (gamma) Specify the initial value of the conversion factor of the fast transversal filter. If the Method property is set to 'Fast transversal least-squares', this property must be a positive numeric value less than or equal to 1. In this case, the default value is 1. If the Method property is set to 'Sliding-window fast transversal least-squares', this property must be a 2-element numeric vector. The first element of this vector must lie within the range [0,1], and the second element must be less than or equal to -1. In this case, the default value is [1, -1]. This property is tunable. InitialCoefficients Initial coefficients of the filterSpecify the initial values of the FIR adaptive filter coefficients as a scalar or a vector of length equal to the value of the Length property. The default value is 0. This property is tunable. LockCoefficients Locked status of the coefficient updatesSpecify whether to lock the filter coefficient values. By default, the value of this property is false, and the object continuously updates the filter coefficients. If this property is set to true, the filter coefficients do not update and their values remain the same. This property is tunable.

## Methods

 msesim Mean-square error for Fast Transversal filter reset Reset filter states for Fast Transversal filter step Apply Fast Transversal filter to input
Common to All System Objects
clone

Create System object with same property values

getNumInputs

Expected number of inputs to a System object

getNumOutputs

Expected number of outputs of a System object

isLocked

Check locked states of a System object (logical)

release

Allow System object property value changes

## Examples

expand all

Note: This example runs only in R2016b or later. If you are using an earlier release, replace each call to the function with the equivalent step syntax. For example, myObject(x) becomes step(myObject,x).

ftf1 = dsp.FastTransversalFilter(11,'ForgettingFactor',0.99);
filt = dsp.FIRFilter;
filt.Numerator = fir1(10,.25);
x = randn(1000,1);
d = filt(x) + 0.01*randn(1000,1);
[y,e] = ftf1(x,d);
w = ftf1.Coefficients;
subplot(2,1,1);
plot(1:1000,[d,y,e]);
title('System Identification of an FIR filter');
legend('Desired','Output','Error');
xlabel('time index');
ylabel('signal value');
subplot(2,1,2);
stem([filt.Numerator; w].');
legend('Actual','Estimated');
xlabel('coefficient #');
ylabel('coefficient value');

## References

[1] Haykin, Simon. Adaptive Filter Theory, 4th Ed. Upper Saddle River, NJ: Prentice Hall, 2002

## See Also

#### Introduced in R2013b

Was this topic helpful?

Watch now