| Titre : |
Adaptive Filters |
| Type de document : |
texte imprimé |
| Auteurs : |
Ali H. Sayed, Auteur |
| Editeur : |
Hoboken, New Jersey : IEEE Press/Wiley |
| Année de publication : |
2008 |
| Importance : |
786 p. |
| Présentation : |
couv. ill. en coul., ill. |
| Format : |
26,1 cm. |
| ISBN/ISSN/EAN : |
978-0-470-25388-5 |
| Langues : |
Anglais (eng) |
| Catégories : |
AUTOMATISME
|
| Index. décimale : |
25-03 Filtrage analogique et numérique |
| Résumé : |
Adaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. This book enables readers to gain a gradual and solid introduction to the subject, its applications to a variety of topical problems, existing limitations, and extensions of current theories. The book consists of eleven parts?each part containing a series of focused lectures and ending with bibliographic comments, problems, and computer projects with MATLAB solutions. |
| Note de contenu : |
Contents
BACKGROUND MATERIAL
A. Random Variables.
B. Linear Algebra
C. Complex Gradients
PART I: OPTIMAL ESTIMATION.
1. Scalar-Valued Data.
2. Vector-Valued Data
PART II: LINEAR ESTIMATION.
3. Normal Equations.
4. Orthogonality Principle.
5. Linear Models.
6. Constrained Estimation.
7. Kalman Filter.
PART III: STOCHASTIC GRADIENT ALGORITHMS.
8. Steepest-Descent Technique.
9. Transient Behavior.
10. LMS Algorithm.
11. Normalized LMS Algorithm.
12. Other LMS-Type Algorithms.
13. Affine Projection Algorithm.
14. RLS Algorithm.
PART IV: MEAN-SQUARE PERFORMANCE.
15. Energy Conservation.
16. Performance of LMS.
17. Performance of NLMS.
18. Performance of Sign-Error LMS.
19. Performance of RLS and Other Filters.
20. Nonstationary Environments.
21. Tracking Performance
PART V: TRANSIENT PERFORMANCE.
22. Weighted Energy Conservation.
23. LMS with Gaussian Regressors.
24. LMS with non-Gaussian Regressors.
25. Data-Normalized Filters.
PART VI: BLOCK ADAPTIVE FILTERS.
26. Transform Domain Adaptive Filters.
27. Efficient Block Convolution.
28. Block and Subband Adaptive Filters.
PART VII: LEAST-SQUARES METHODS.
29. Least-Squares Criterion.
30. Recursive Least-Squares.
31. Kalman Filtering and RLS.
32. Order and Time-Update Relations.
PART VIII: ARRAY ALGORITHMS.
33. Norm and Angle Preservation.
34. Unitary Transformations.
35. QR and Inverse QR Algorithms.
PART IX: FAST RLS ALGORITHMS.
36. Hyperbolic Rotations.
37. Fast Array Algorithm.
38. Regularized Prediction Problems.
39. Fast Fixed-Order Filters.
PART X: LATTICE FILTERS.
40. Three Basic Estimation Problems.
41. Lattice Filter Algorithms.
42. Error-Feedback Lattice Filters.
43. Array Lattice Filters.
PART XI: ROBUST FILTERS.
44. Indefinite Least-Squares.
45. Robust Adaptive Filters.
46. Robustness Properties.
-Index.
|
Adaptive Filters [texte imprimé] / Ali H. Sayed, Auteur . - Hoboken, New Jersey : IEEE Press/Wiley, 2008 . - 786 p. : couv. ill. en coul., ill. ; 26,1 cm. ISBN : 978-0-470-25388-5 Langues : Anglais ( eng)
| Catégories : |
AUTOMATISME
|
| Index. décimale : |
25-03 Filtrage analogique et numérique |
| Résumé : |
Adaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. This book enables readers to gain a gradual and solid introduction to the subject, its applications to a variety of topical problems, existing limitations, and extensions of current theories. The book consists of eleven parts?each part containing a series of focused lectures and ending with bibliographic comments, problems, and computer projects with MATLAB solutions. |
| Note de contenu : |
Contents
BACKGROUND MATERIAL
A. Random Variables.
B. Linear Algebra
C. Complex Gradients
PART I: OPTIMAL ESTIMATION.
1. Scalar-Valued Data.
2. Vector-Valued Data
PART II: LINEAR ESTIMATION.
3. Normal Equations.
4. Orthogonality Principle.
5. Linear Models.
6. Constrained Estimation.
7. Kalman Filter.
PART III: STOCHASTIC GRADIENT ALGORITHMS.
8. Steepest-Descent Technique.
9. Transient Behavior.
10. LMS Algorithm.
11. Normalized LMS Algorithm.
12. Other LMS-Type Algorithms.
13. Affine Projection Algorithm.
14. RLS Algorithm.
PART IV: MEAN-SQUARE PERFORMANCE.
15. Energy Conservation.
16. Performance of LMS.
17. Performance of NLMS.
18. Performance of Sign-Error LMS.
19. Performance of RLS and Other Filters.
20. Nonstationary Environments.
21. Tracking Performance
PART V: TRANSIENT PERFORMANCE.
22. Weighted Energy Conservation.
23. LMS with Gaussian Regressors.
24. LMS with non-Gaussian Regressors.
25. Data-Normalized Filters.
PART VI: BLOCK ADAPTIVE FILTERS.
26. Transform Domain Adaptive Filters.
27. Efficient Block Convolution.
28. Block and Subband Adaptive Filters.
PART VII: LEAST-SQUARES METHODS.
29. Least-Squares Criterion.
30. Recursive Least-Squares.
31. Kalman Filtering and RLS.
32. Order and Time-Update Relations.
PART VIII: ARRAY ALGORITHMS.
33. Norm and Angle Preservation.
34. Unitary Transformations.
35. QR and Inverse QR Algorithms.
PART IX: FAST RLS ALGORITHMS.
36. Hyperbolic Rotations.
37. Fast Array Algorithm.
38. Regularized Prediction Problems.
39. Fast Fixed-Order Filters.
PART X: LATTICE FILTERS.
40. Three Basic Estimation Problems.
41. Lattice Filter Algorithms.
42. Error-Feedback Lattice Filters.
43. Array Lattice Filters.
PART XI: ROBUST FILTERS.
44. Indefinite Least-Squares.
45. Robust Adaptive Filters.
46. Robustness Properties.
-Index.
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Adaptive Filters
