1 / 34

DSP-CIS Chapter-12: Least Mean Squares (LMS) Algorithm

DSP-CIS Chapter-12: Least Mean Squares (LMS) Algorithm. Marc Moonen Dept. E.E./ESAT, KU Leuven marc.moonen@esat.kuleuven.be www.esat.kuleuven.be / scd /. Part-III : Optimal & Adaptive Filters. : Optimal & Adaptive Filters - Intro General Set-Up Applications

quinn-heath
Download Presentation

DSP-CIS Chapter-12: Least Mean Squares (LMS) Algorithm

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. DSP-CISChapter-12: Least Mean Squares (LMS) Algorithm Marc Moonen Dept. E.E./ESAT, KU Leuven marc.moonen@esat.kuleuven.be www.esat.kuleuven.be/scd/

  2. Part-III : Optimal & Adaptive Filters : Optimal & Adaptive Filters - Intro • General Set-Up • Applications • Optimal (Wiener) Filters • : Least Squares & Recursive Least Squares Estimation • Least Squares Estimation • Recursive Least Squares (RLS) Estimation • Square-Root Algorithms • : Least Means Squares (LMS) Algorithm • LMS/NLMS : Stochastic Gradient Algorithms • LMS analysis • LMS Family • : Fast Recursive Least Squares Algorithms : Kalman Filtering Chapter-11 Chapter-12 Chapter-13 Chapter-14 Chapter-15

  3. Least Mean Squares (LMS) Algorithm

  4. Least Mean Squares (LMS) Algorithm

  5. Least Mean Squares (LMS) Algorithm (Widrow 1965 !!)

  6. Least Mean Squares (LMS) Algorithm Bernard Widrow

  7. Least Mean Squares (LMS) Algorithm

  8. Least Mean Squares (LMS) Algorithm

  9. Least Mean Squares (LMS) Algorithm  large λ_max implies a small stepsize

  10. Least Mean Squares (LMS) Algorithm

  11. Least Mean Squares (LMS) Algorithm error vector projected onto eigenvectors initial error vector projected onto eigenvectors (=projection on i-th eigenvector) • small λ_i implies slow convergence • λ_min <<λ_max (hence small μ) implies *very* slow convergence

  12. Least Mean Squares (LMS) Algorithm

  13. Least Mean Squares (LMS) Algorithm

  14. Least Mean Squares (LMS) Algorithm

  15. Least Mean Squares (LMS) Algorithm

  16. Least Mean Squares (LMS) Algorithm

  17. Least Mean Squares (LMS) Algorithm

  18. Least Mean Squares (LMS) Algorithm

  19. Least Mean Squares (LMS) Algorithm

  20. Least Mean Squares (LMS) Algorithm

  21. Least Mean Squares (LMS) Algorithm

  22. Least Mean Squares (LMS) Algorithm

  23. Least Mean Squares (LMS) Algorithm

  24. Least Mean Squares (LMS) Algorithm

  25. Least Mean Squares (LMS) Algorithm

  26. Least Mean Squares (LMS) Algorithm

  27. Least Mean Squares (LMS) Algorithm

  28. Least Mean Squares (LMS) Algorithm

  29. Least Mean Squares (LMS) Algorithm

  30. Least Mean Squares (LMS) Algorithm

  31. Least Mean Squares (LMS) Algorithm

  32. Least Mean Squares (LMS) Algorithm

  33. Least Mean Squares (LMS) Algorithm

  34. Least Mean Squares (LMS) Algorithm

More Related