Apply a LMS Algorithm to System Identification, Part 1

Learn how to apply the least mean squares (LMS) algorithm to the problem of system identification. The LMS algorithm is the most popular adaptive algorithm in the world because of its simplicity and flexibility. You’ll learn how to set up a Simulink® model to perform system identification and verify its performance using visualization tools such as scopes and array plots. The focus is on rapid prototyping, i.e., constructing a system identification model with the least amount of effort. The LMS Filter block from DSP System Toolbox™ is leveraged for this purpose. In later videos, you’ll see how to construct your own LMS-based adaptive systems from the ground up. Learn more: - Adaptive Filters: https://bit.ly/339uGor - System Identification of FIR Filter Using LMS Algorithm: https://bit.ly/3MVFMp9 - System Identification of FIR Filter Using Normalized LMS Algorithm: https://bit.ly/4do31To - LMS Filter: https://bit.ly/3MXM0EV - Compare RLS and LMS Adaptive Filter Algorithms: https://bit.ly/3zEztDa - Recursive Algorithms for Online Parameter Estimation: https://bit.ly/4eA8o34 Chapters: 00:00 Introduction 00:25 Walk Through the SysID Model 05:30 Run the Model to Identify FIR System 06:15 Run the Model to Identify IIR System 07:50 Increase Number of LMS Taps 09:42 Limitations and Future Work 10:58 Why Not Use an Impulse as the Excitation? -------------------------------------------------------------------------------------------------------- Get a free product trial: https://goo.gl/ZHFb5u Learn more about MATLAB: https://goo.gl/8QV7ZZ Learn more about Simulink: https://goo.gl/nqnbLe See what's new in MATLAB and Simulink: https://goo.gl/pgGtod © 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc. See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.