Least Mean Square (LMS)
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 LMS algorithm. This implementation of 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.
Citation pour cette source
Shujaat Khan (2025). Least Mean Square (LMS) (https://www.mathworks.com/matlabcentral/fileexchange/60080-least-mean-square-lms), MATLAB Central File Exchange. Extrait(e) le .
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- Signal Processing > Signal Processing Toolbox > Digital and Analog Filters > Digital Filter Design > Adaptive Filters >
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Inspiré par : Gradient Descent Method (Least Mean Square) demonstration
A inspiré : Constrain Least Mean Square Algorithm
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Version | Publié le | Notes de version | |
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1.0.0.0 | Description update |