File Exchange

image thumbnail

System Identification using least mean square (LMS) algorithm

version (2.82 KB) by Shujaat Khan
This is the simplest example of system identification using LMS algorithm.


Updated 12 Jan 2018

View Version History

View License

In this simulation, I just used the one algorithm named as least mean square (LMS) for the system identification task. It is designed for those who are new to adaptive signal processing. You can modify this example for CLMS, NLMS, LMF, qLMS or even to FLMS etc very easily.

Cite As

Shujaat Khan (2021). System Identification using least mean square (LMS) algorithm (, MATLAB Central File Exchange. Retrieved .

Comments and Ratings (7)

Sonalika Singh

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?


What is the difference between system identification and blind system identification.

Shujaat Khan

Here the purpose of adding noise in desired output signal is to simulate the measurement noise scenario. If you add noise in input signal then you will get the response of the system for that noisy signal, which is not equals to the measurement noise.

Rajkumar Rajavel

Why is input the original signal and desired signal is noise-corrupted signal? Is that correct? Please help soon.

Michal Krepa

thanks, useful script!

MATLAB Release Compatibility
Created with R2014b
Compatible with any release
Platform Compatibility
Windows macOS Linux

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!