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alg = normlms(stepsize)
alg = normlms(stepsize,bias)
The normlms function creates an adaptive algorithm object that you can use with the lineareq function or dfe function to create an equalizer object. You can then use the equalizer object with the equalize function to equalize a signal. To learn more about the process for equalizing a signal, see Adaptive Algorithms.
alg = normlms(stepsize) constructs an adaptive algorithm object based on the normalized least mean square (LMS) algorithm with a step size of stepsize and a bias parameter of zero.
alg = normlms(stepsize,bias) sets the bias parameter of the normalized LMS algorithm. bias must be between 0 and 1. The algorithm uses the bias parameter to overcome difficulties when the algorithm's input signal is small.
The table below describes the properties of the normalized LMS adaptive algorithm object. To learn how to view or change the values of an adaptive algorithm object, see Access Properties of an Adaptive Algorithm.
Property | Description |
---|---|
AlgType | Fixed value, 'Normalized LMS' |
StepSize | LMS step size parameter, a nonnegative real number |
LeakageFactor | LMS leakage factor, a real number between 0 and 1. A value of 1 corresponds to a conventional weight update algorithm, while a value of 0 corresponds to a memoryless update algorithm. |
Bias | Normalized LMS bias parameter, a nonnegative real number |