Proportionate-Type Normalized Least Mean Square Algorithms:
The topic of this book is proportionate-type normalized least mean squares (PtNLMS) adaptive filtering algorithms, which attempt to estimate an unknown impulse response by adaptively giving gains proportionate to an estimate of the impulse response and the current measured error. These algorithms of...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Somerset
John Wiley & Sons, Incorporated
2013
|
| Ausgabe: | 1st ed |
| Schriftenreihe: | FOCUS Series
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| Schlagworte: | |
| Zusammenfassung: | The topic of this book is proportionate-type normalized least mean squares (PtNLMS) adaptive filtering algorithms, which attempt to estimate an unknown impulse response by adaptively giving gains proportionate to an estimate of the impulse response and the current measured error. These algorithms offer low computational complexity and fast convergence times for sparse impulse responses in network and acoustic echo cancellation applications. New PtNLMS algorithms are developed by choosing gains that optimize user-defined criteria, such as mean square error, at all times. PtNLMS algorithms are extended from real-valued signals to complex-valued signals. The computational complexity of the presented algorithms is examined. Contents 1. Introduction to PtNLMS Algorithms 2. LMS Analysis Techniques 3. PtNLMS Analysis Techniques 4. Algorithms Designed Based on Minimization of User Defined Criteria 5. Probability Density of WD for PtLMS Algorithms 6. Adaptive Step-size PtNLMS Algorithms 7. Complex PtNLMS Algorithms 8. Computational Complexity for PtNLMS Algorithms About the Authors Kevin Wagner has been a physicist with the Radar Division of the Naval Research Laboratory, Washington, DC, USA since 2001. His research interests are in the area of adaptive signal processing and non-convex optimization. Milos Doroslovacki has been with the Department of Electrical and Computer Engineering at George Washington University, USA since 1995, where he is now an Associate Professor. His main research interests are in the fields of adaptive signal processing, communication signals and systems, discrete-time signal and system theory, and wavelets and their applications |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 online resource (155 pages) |
| ISBN: | 9781118579251 9781848214705 |
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| 520 | |a The topic of this book is proportionate-type normalized least mean squares (PtNLMS) adaptive filtering algorithms, which attempt to estimate an unknown impulse response by adaptively giving gains proportionate to an estimate of the impulse response and the current measured error. These algorithms offer low computational complexity and fast convergence times for sparse impulse responses in network and acoustic echo cancellation applications. New PtNLMS algorithms are developed by choosing gains that optimize user-defined criteria, such as mean square error, at all times. PtNLMS algorithms are extended from real-valued signals to complex-valued signals. The computational complexity of the presented algorithms is examined. Contents 1. Introduction to PtNLMS Algorithms 2. LMS Analysis Techniques 3. PtNLMS Analysis Techniques 4. Algorithms Designed Based on Minimization of User Defined Criteria 5. Probability Density of WD for PtLMS Algorithms 6. Adaptive Step-size PtNLMS Algorithms 7. Complex PtNLMS Algorithms 8. Computational Complexity for PtNLMS Algorithms About the Authors Kevin Wagner has been a physicist with the Radar Division of the Naval Research Laboratory, Washington, DC, USA since 2001. His research interests are in the area of adaptive signal processing and non-convex optimization. Milos Doroslovacki has been with the Department of Electrical and Computer Engineering at George Washington University, USA since 1995, where he is now an Associate Professor. His main research interests are in the fields of adaptive signal processing, communication signals and systems, discrete-time signal and system theory, and wavelets and their applications | ||
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| isbn | 9781118579251 9781848214705 |
| language | English |
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| spelling | Wagner, Kevin Verfasser aut Proportionate-Type Normalized Least Mean Square Algorithms 1st ed Somerset John Wiley & Sons, Incorporated 2013 © 2013 1 online resource (155 pages) txt rdacontent c rdamedia cr rdacarrier FOCUS Series Description based on publisher supplied metadata and other sources The topic of this book is proportionate-type normalized least mean squares (PtNLMS) adaptive filtering algorithms, which attempt to estimate an unknown impulse response by adaptively giving gains proportionate to an estimate of the impulse response and the current measured error. These algorithms offer low computational complexity and fast convergence times for sparse impulse responses in network and acoustic echo cancellation applications. New PtNLMS algorithms are developed by choosing gains that optimize user-defined criteria, such as mean square error, at all times. PtNLMS algorithms are extended from real-valued signals to complex-valued signals. The computational complexity of the presented algorithms is examined. Contents 1. Introduction to PtNLMS Algorithms 2. LMS Analysis Techniques 3. PtNLMS Analysis Techniques 4. Algorithms Designed Based on Minimization of User Defined Criteria 5. Probability Density of WD for PtLMS Algorithms 6. Adaptive Step-size PtNLMS Algorithms 7. Complex PtNLMS Algorithms 8. Computational Complexity for PtNLMS Algorithms About the Authors Kevin Wagner has been a physicist with the Radar Division of the Naval Research Laboratory, Washington, DC, USA since 2001. His research interests are in the area of adaptive signal processing and non-convex optimization. Milos Doroslovacki has been with the Department of Electrical and Computer Engineering at George Washington University, USA since 1995, where he is now an Associate Professor. His main research interests are in the fields of adaptive signal processing, communication signals and systems, discrete-time signal and system theory, and wavelets and their applications Algorithms Computer algorithms Equations, Simultaneous -- Numerical solutions Doroslovacki, Milos Sonstige oth Erscheint auch als Druck-Ausgabe Wagner, Kevin Proportionate-Type Normalized Least Mean Square Algorithms |
| spellingShingle | Wagner, Kevin Proportionate-Type Normalized Least Mean Square Algorithms Algorithms Computer algorithms Equations, Simultaneous -- Numerical solutions |
| title | Proportionate-Type Normalized Least Mean Square Algorithms |
| title_auth | Proportionate-Type Normalized Least Mean Square Algorithms |
| title_exact_search | Proportionate-Type Normalized Least Mean Square Algorithms |
| title_full | Proportionate-Type Normalized Least Mean Square Algorithms |
| title_fullStr | Proportionate-Type Normalized Least Mean Square Algorithms |
| title_full_unstemmed | Proportionate-Type Normalized Least Mean Square Algorithms |
| title_short | Proportionate-Type Normalized Least Mean Square Algorithms |
| title_sort | proportionate type normalized least mean square algorithms |
| topic | Algorithms Computer algorithms Equations, Simultaneous -- Numerical solutions |
| topic_facet | Algorithms Computer algorithms Equations, Simultaneous -- Numerical solutions |
| work_keys_str_mv | AT wagnerkevin proportionatetypenormalizedleastmeansquarealgorithms AT doroslovackimilos proportionatetypenormalizedleastmeansquarealgorithms |