System Identification Using Least Mean Forth (LMF) and Least Mean Square (LMS) algorithm
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.
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Shujaat Khan (2024). 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. Abgerufen .
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Inspiriert von: Add white Uniform noise to a signal, System Identification Using Recursive Least Square (RLS) and Least Mean Square (LMS) algorithm
Inspiriert: Variable Step-Size Least Mean Square (VSS-LMS) Algorithm
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Version | Veröffentlicht | Versionshinweise | |
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1.2.0.0 | - Example |
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1.1.0.0 | - Monte Carlo simulation setup |
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1.0.0.0 | - Signal generator is generalized
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