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Least Mean Square for System Identification

version 1.0.0.0 (2.49 KB) by Santhana Raj
Least Mean Square (LMS) used for system identification.

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Updated 10 Sep 2014

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Least mean squares (LMS) algorithms are a class of adaptive filter used to mimic a desired filter by finding the filter coefficients that relate to producing the least mean squares of the error signal (difference between the desired and the actual signal).
The weights update Formula for every Iteration is Wt new=Wt old + mu * error *input;
For Example:
inp=wavread('BlueFunk-bass1.wav');
inp=inp-(min(inp)); % required for audio signals
h=[1 -4 6 -5 2]; % Known System papametes for a low Pass filter h
Iter=lms(inp,h,100,5,1);

Author:Santhana Raj.A https://sites.google.com/site/santhanarajarunachalam/

Cite As

Santhana Raj (2022). Least Mean Square for System Identification (https://www.mathworks.com/matlabcentral/fileexchange/47790-least-mean-square-for-system-identification), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2012a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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