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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Mise à jour 22 fév. 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.

Citation pour cette source

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. Récupéré le .

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Créé avec R2011a
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Plant_Identification_LMS_LMF/

Plant_Identification_LMS_LMF/html/

Version Publié le Notes de version
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