1 / 38

Widrow -Hoff Learning

Widrow -Hoff Learning. Outline. 1 Introduction 2 ADALINE Network 3 Mean Square Error 4 LMS Algorithm 5 Analysis of Converge 6 Adaptive Filtering. Introduction.

laurie
Télécharger la présentation

Widrow -Hoff Learning

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. Widrow-Hoff Learning

  2. Outline • 1 Introduction • 2 ADALINE Network • 3 Mean Square Error • 4 LMS Algorithm • 5 Analysis of Converge • 6 Adaptive Filtering

  3. Introduction • In 1960, Bernard Widrow and his doctoral student Marcian Hoff introduced the ADALINE (ADAptiveLInearNEuron)network and LMS(Least Mean Square) algorithm.

  4. Perceptron Network • Figure: a=hardlim(Wp+b)

  5. ADALINE Network • Figure: a=purelin(Wp+b)=Wp+b

  6. Single ADALINE

  7. decision boundary

  8. Mean Square Error

  9. Mean Square Error(conti.)

  10. Mean Square Error(conti.)

  11. Error analysis

  12. Error analysis(conti.) d = -2h and A = 2R definite = 0

  13. Example 1

  14. Example 1(conti.)

  15. Example 1(conti.)

  16. Approximate Steepest Descent

  17. Approximate Gradient

  18. Approximate Gradient(conti.)

  19. Approximate Gradient(conti.)

  20. LMS Algorithm

  21. LMS Algorithm (conti.)

  22. Example 2

  23. Example 2(conti.) , W(0)=

  24. Example 2(conti.)

  25. Example 2(conti.)

  26. Example 2(conti.)

  27. Analysis of Convergence

  28. Analysis of Convergence(conti.)

  29. Analysis of Convergence(conti.)

  30. Example 3

  31. Perceptron rule V.S. LMS algorithm

  32. Perceptron rule V.S. LMS algorithm(conti.)

  33. Perceptron rule V.S. LMS algorithm(conti.)

  34. Perceptron rule V.S. LMS algorithm(conti.)

  35. Adaptive Filtering

  36. Tapped Delay Line

  37. Adaptive Filter

  38. Adaptive Noise Cancellation

More Related