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Constrain Least Mean Square Algorithm

version 1.0.0 (1.74 KB) by Shujaat Khan
constrain least mean square with L1 and L2 constrains for regression problem

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Updated 30 Sep 2019

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In this code, a linear equation is used to generate sample data using a slope and bias. Later a Gaussian noise is added to the desired output. The noisy output and original input is used to determine the slope and bias of the linear equation using constrain-LMS algorithm. This implementation of constrain-LMS is based on batch update rule of gradient decent algorithm in which we use the sum of error instead of sample error. You can modify this code to create sample based update rule easily. There are three options of constrain I implemented in this code 'None', 'L1', and 'L2'. You can also change input/noise signal distributions as well to see which constrain work best for which type of signal/noise.

Cite As

Shujaat Khan (2020). Constrain Least Mean Square Algorithm (https://www.mathworks.com/matlabcentral/fileexchange/72899-constrain-least-mean-square-algorithm), MATLAB Central File Exchange. Retrieved .

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MATLAB Release Compatibility
Created with R2019b
Compatible with any release
Platform Compatibility
Windows macOS Linux
Acknowledgements

Inspired by: Least Mean Square (LMS)