Designing Adaptive Filters (Filter Design Toolbox)

Reports the algorithm the object uses for adaptation. When you construct you adaptive filter object, this property is set automatically. You cannot change the value--it is read only.

Averaging factor used to compute the exponentially-windowed estimates of the powers in the transformed signal bins for the coefficient updates. AvgFactor should lie between zero and one. For default filter objects, AvgFactor equals (1 - step). lambda is the input argument that represent AvgFactor

Block length for the coefficient updates. This must be a positive integer such that (l/blocklen) is also an integer. For faster execution, blocklen should be a power of two. blocklen defaults to two.

Vector containing the initial filter coefficients. It must be a length l vector where l is the number of filter coefficients. coeffs defaults to length l vector of zeros when you do not provide the argument for input.

Conversion factor defaults to the matrix [1 -1] that specifies soft-constrained initialization. This is the gamma input argument for some of the fast transversal algorithms.

Update delay given in time samples. This scalar should be a positive integer--negative delays do not work. delay defaults to 1 for most algorithms.

Desired signal states of the adaptive filter. dstates defaults to a zero vector with length equal to (blocklen - 1) or (swblocklen - 1) depending on the algoritm.

Vector of the epsilon values of the adaptive filter. EpsilonStates defaults to a vector of zeros with (projectord - 1) elements.

Vector of the adaptive filter error states. ErrorStates defaults to a zero vector with length equal to (projectord - 1).

Contains the length of the filter. Note that this is not the filter order. Filter length is 1 greater than filter order. Thus a filter with length equal to 10 has filter order equal to 9.

Determines how the RLS adaptive filter uses past data in each iteration. You use the forgetting factor to specify whether old data carries the same weight in the algorithm as more recent data.

This is a scalar and should lie in the range (0, 1]. It defaults to 1. Setting forgetting factor = 1 denotes infinite memory while adapting to find the new filter. Note that this is the lambda input argument.

Upper-triangular Cholesky (square root) factor of the input covariance matrix. Initialize this matrix with a positive definite upper triangular matrix. Dimensions are l-by-l, where l is the filter length.

Empty when you construct the object, this gets populated after you run the filter.

Contains the setting for leakage in the adaptive filter algorithm. Using a leakage factor that is not 1 forces the weights to adapt even when they hve found the minimum error solution. Forcing the adaptation can improve the numerical performance of the LMS algorithm.

Reports the number of input samples processed by your adaptfilt object when you filter a data set. When you set the ResetBeforeFiltering property to on, NumSamplesProcessed reports the number of processed input samples for your most recent filtering operation with the object.

With ResetBeforeFiltering set to off, NumSamplesProcessed accumulates the total number of samples processed for all preceding filtering operations.

Specifies an optional offset for the denominator of the step size normalization term. You must specify offset to be a scalar greater than or equal to zero. Nonzero offsets can help avoid a divide-by-near-zero condition that causes errors.

Use this to avoid dividing by zero or by very small numbers when input signal amplitude becomes very small, or dividing by very small numbers when any of the FFT input signal powers become very small. offset defaults to one.

A vector of 2*l elements, each initialized with the value delta from the unput arguments. As you filter data, Power gets updated by the filter process.

Projection order of the affine projection algorithm. projectord defines the size of the input signal covariance matrix and defaults to two.

Determine whether the filter states and coefficients get restored to their starting values for each filtering operation. The starting values are the values in place when you create the filter.

ResetBeforeFiltering returns to zero any property value that the filter changes during processing. Property values that the filter does not change are not affected. Defaults to 'on'.

A vector that contains the coefficient values of your secondary path from the output actuator to the error sensor.

Upper-triangular Cholesky (square root) factor of the input covariance matrix. Initialize this matrix with a positive definite upper triangular matrix.

Square root of the inverse of the sliding window input signal covariance matrix. This square matrix should be full-ranked.

Vector of the adaptive filter states. states defaults to a vector of zeros whose length depends on the chosen algorithm. Usually the length is a function of the filter length l and another input argument to the filter object, such as projectord.

Reports the size of the step taken between interations of the adaptive filter process. Each adaptfilt object has a default value that best meets the needs of the algorithm.

Block length of the sliding window. This integer must be at least as large as the filter length. swblocklen defaults to 16.

[1] Hayes, Monson H., Statistical Digital Signal Processing and Modeling, John Wiley & Sons, 1996, 493-552.

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