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.