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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 (2021). System Identification Using Least Mean Forth (LMF) and Least Mean Square (LMS) algorithm (https://www.mathworks.com/matlabcentral/fileexchange/63596-system-identification-using-least-mean-forth-lmf-and-least-mean-square-lms-algorithm), MATLAB Central File Exchange. Retrieved .

Comments and Ratings (5)

Abdelwahab Afifi

When I use the Algorithm in a complex system where the input and the output are complex. I didn't get the expected results/curves. Does the command need to be modified adapted to the complex values?

varsha

Sir can you please suggest some papers on blind system identification in time varying system

Shujaat Khan

Thank you @jing zhang and @Rui Yang

Rui Yang

jing zhang

MATLAB Release Compatibility
Created with R2011a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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