System Identification Using Least Mean Forth (LMF) and Least Mean Square (LMS) algorithm
System Identification Using Least Mean Forth (LMF) and Least Mean Square (LMS) algorithm
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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.
Citation pour cette source
Shujaat Khan (2026). System Identification Using Least Mean Forth (LMF) and Least Mean Square (LMS) algorithm (https://fr.mathworks.com/matlabcentral/fileexchange/63596-system-identification-using-least-mean-forth-lmf-and-least-mean-square-lms-algorithm), MATLAB Central File Exchange. Extrait(e) le .
Informations générales
- Version 1.2.0.0 (117 ko)
Compatibilité avec les versions de MATLAB
- Compatible avec toutes les versions
Plateformes compatibles
- Windows
- macOS
- Linux
| Version | Publié le | Notes de version | Action |
|---|---|---|---|
| 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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