System Identification Using LMS Algorithm and Huber Cost Function Minimization

Version 1.0.0.0 (2.23 MB) by Sambit Behura
Modelling a FIR Filter using LMS Algorithm and, Huber's Cost Function for presence of outliers
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Updated 15 Feb 2018

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Modelling a FIR Filter using LMS Algorithm and, Huber's Cost Function Minimization for presence of a certain percentage of outliers.
Here we have to identify and model a 3-tap FIR filter with weights [0.26 0.93 0.26].
This has to be done using:
1) Mean Square error minimization (LMS Algorithm)-
The reference signal is corrupted by additive white gaussian noise (mean=0, standard deviation=0.1)
2) Huber Loss Minimization (with 10 to 20 percent outlier added to the noise)
The reference signal is corrupted by additive white gaussian noise (mean=0, standard deviation=0.05)

Cite As

Sambit Behura (2026). System Identification Using LMS Algorithm and Huber Cost Function Minimization (https://ch.mathworks.com/matlabcentral/fileexchange/65901-system-identification-using-lms-algorithm-and-huber-cost-function-minimization), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2017a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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System Identification Using LMS Algorithm and Huber Cost Function Minimization/Huber Cost Function/

System Identification Using LMS Algorithm and Huber Cost Function Minimization/LMS Error Cost Function/

Version Published Release Notes
1.0.0.0

Problem Statement Updated

Problem Statement Updated