System Identification Using Least Mean Forth (LMF) and Least Mean Square (LMS) algorithm

Version 1.2.0.0 (117 KB) by Shujaat Khan
System Identification Using Least Mean Forth (LMF) and Least Mean Square (LMS) algorithm
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Updated 22 Feb 2018

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In this simulation least mean square (LMS) and least mean forth (LMF) algorithms are compared in non-Gaussian noisy environment for system identification task. Is it well known that the LMF algorithm outperforms the LMS algorithm in non-Gaussian environment, the same results can be seen in this implementation. Additionally a customized function for additive white uniform noise is also programmed.

Cite As

Shujaat Khan (2026). System Identification Using Least Mean Forth (LMF) and Least Mean Square (LMS) algorithm (https://au.mathworks.com/matlabcentral/fileexchange/63596-system-identification-using-least-mean-forth-lmf-and-least-mean-square-lms-algorithm), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2011a
Compatible with any release
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Version Published Release Notes
1.2.0.0

- Example
1.1.0.0

- Monte Carlo simulation setup
1.0.0.0

- Signal generator is generalized
- results on arbitrary system are shown