Catalogue


Séminaire Bourbaki, vol. 1978/79, Exposés 525-542.
Séminaire Bourbaki (31st : 1978-1979)
imprint
Berlin ; New York : Springer-Verlag, 1980.
description
iv, 341 p. : ill. ; 25 cm.
ISBN
0387097333
format(s)
Book
Holdings
Subjects
personal subject
More Details
imprint
Berlin ; New York : Springer-Verlag, 1980.
isbn
0387097333
general note
"Avec table par noms d'auteurs de 1967/68 á 1978/79."
Contributions in French or English.
catalogue key
2268832
 
Includes bibliographies.
A Look Inside
Reviews
Review Quotes
From the reviews: "Starting with a review of the history and the essential concepts in (numerical) linear algebra that are essential in the development of QR decomposition, the book continues with an overview of adaptive filtering techniques, including LMS and RLS algorithms. ... The chapters flow on nicely from one to the other, and the editor is to be congratulated on achieving this. The book is a useful and welcome contribution to the broad topic of numerical linear algebra." (Andrew Dale, Zentralblatt MATH, Vol. 1170, 2009)
From the reviews: Starting with a review of the history and the essential concepts in (numerical) linear algebra that are essential in the development of QR decomposition, the book continues with an overview of adaptive filtering techniques, including LMS and RLS algorithms. … The chapters flow on nicely from one to the other, and the editor is to be congratulated on achieving this. The book is a useful and welcome contribution to the broad topic of numerical linear algebra. (Andrew Dale, Zentralblatt MATH, Vol. 1170, 2009)
From the reviews:Starting with a review of the history and the essential concepts in (numerical) linear algebra that are essential in the development of QR decomposition, the book continues with an overview of adaptive filtering techniques, including LMS and RLS algorithms. … The chapters flow on nicely from one to the other, and the editor is to be congratulated on achieving this. The book is a useful and welcome contribution to the broad topic of numerical linear algebra. (Andrew Dale, Zentralblatt MATH, Vol. 1170, 2009)
To find out how to look for other reviews, please see our guides to finding book reviews in the Sciences or Social Sciences and Humanities.
Summaries
Back Cover Copy
QRD-RLS Adaptive Filtering covers some of the most recent developments as well as the basic concepts for a complete understanding of the QRD-RLS-based adaptive filtering algorithms. It presents this research with a clear historical perspective which highlights the underpinning theory and common motivating factors that have shaped the subject. The material is divided into twelve chapters, going from fundamentals to more advanced aspects. Different algorithms are derived and presented, including basic, fast, lattice, multichannel and constrained versions. Important issues, such as numerical stability, performance in finite precision environments and VLSI oriented implementations are also addressed. All algorithms are derived using Givens rotations, although one chapter deals with implementations using Householder reflections. QRD-RLS Adaptive Filtering is a useful reference for engineers and academics in the field of adaptive filtering.
Back Cover Copy
QRD-RLS Adaptive Filtering covers some of the most recent developments as well as the basic concepts for a complete understanding of the QRD-RLS-based adaptive filtering algorithms. It presents this research with a clear historical perspective which highlights the underpinning theory and common motivating factors that have shaped the subject.The material is divided into twelve chapters, going from fundamentals to more advanced aspects. Different algorithms are derived and presented, including basic, fast, lattice, multichannel and constrained versions. Important issues, such as numerical stability, performance in finite precision environments and VLSI oriented implementations are also addressed. All algorithms are derived using Givens rotations, although one chapter deals with implementations using Householder reflections.QRD-RLS Adaptive Filtering is a useful reference for engineers and academics in the field of adaptive filtering.
Main Description
QRD-RLS Adaptive Filtering covers some of the most recent developments as well as the basic concepts for a complete understanding of the QRD-RLS-based adaptive filtering algorithms. It presents this research with a clear historical perspective which highlights the underpinning theory and common motivating factors that have shaped the subject. The material is divided into twelve chapters, going from fundamentals to more advanced aspects. Different algorithms are derived and presented, including basic, fast, lattice, multichannel and constrained versions. Important issues, such as numerical stability, performance in finite precision environments and VLSI oriented implementations are also addressed. All algorithms are derived using Givens rotations, although one chapter deals with implementations using Householder reflections. QRD-RLS Adaptive Filtering is a useful reference for engineers and academics in the field of adaptive filtering. Book jacket.
Main Description
This book provides tools and knowledge in a simple way such that the reader is able to implement a particular QRD-RLS algorithm tailored for the application at hand. The material presents not only classical and recent research results but also the expertise of a number of authors that have contributed to the development of this fast converging class of adaptive filter algorithms. The intended audience encompasses graduate students, researchers, and practitioners working in the field of adaptive filtering or in need of using a robust and fast converging algorithm.
Table of Contents
QR Decomposition: An Annotated Bibliographyp. 1
Preamblep. 1
Eigenvalues and Eigenvectorsp. 2
Iterative Methods for the Solution of the Eigenproblemp. 3
The LR Algorithmp. 3
The QR algorithmp. 4
QR Decomposition for Orthogonalizationp. 5
The classical Gram-Schmidt orthogonalization methodp. 6
The modified Gram-Schmidt orthogonalization methodp. 8
Triangularization via Householder reflectionsp. 9
Triangularization via Givens plane rotationsp. 10
QR Decomposition for Linear Least Squares Problemsp. 12
QR Decomposition by systolic arraysp. 14
QR Decomposition for Recursive Least Squares Adaptive Filtersp. 14
Fast QR Decomposition RLS adaptation algorithmsp. 16
Conclusionp. 17
Referencesp. 18
Introduction to Adaptive Filtersp. 23
Basic Conceptsp. 23
Error Measurementsp. 28
The mean-square errorp. 28
The instantaneous square errorp. 29
The weighted least-squaresp. 29
Adaptation Algorithmsp. 30
LMS and normalized-LMS algorithmsp. 31
Data-reusing LMS algorithmsp. 34
RLS-type algorithmsp. 40
Computer Simulationsp. 42
Example 1: Misadjustment of the LMS algorithmp. 42
Example 2: Convergence trajectoriesp. 43
Example 3: Tracking performancep. 43
Example 4: Algorithm stabilityp. 46
Conclusionp. 47
Referencesp. 48
Conventional and Inverse QRD-RLS Algorithmsp. 51
The Least-Squares Problem and the QR Decompositionp. 51
The Givens Rotation Methodp. 57
The Conventional QRD-RLS Algorithmp. 60
Initialization of the Triangularization Procedurep. 64
On the Q¿(k) Matrixp. 66
The backward prediction problemp. 69
The forward prediction problemp. 71
Interpreting the elements of Q¿(k) for a lower triangular Cholesky factorp. 74
Interpreting the elements of Q¿(k) for an upper triangular Cholesky factorp. 75
The Inverse QRD-RLS Algorithmp. 76
Conclusionp. 77
p. 79
p. 80
p. 81
Referencesp. 84
Fast QRD-RLS Algorithmsp. 87
Introductionp. 87
Upper Triangualarization Algorithms (Updating Forward Prediction Errors)p. 89
The FQR_POS_F algorithmp. 90
The FQR_PRI_F algorithmp. 92
Lower Triangularization Algorithms (Updating Backward Prediction Errors)p. 93
The FQR_POS_B algorithmp. 95
The FQR_PRI_B algorithmp. 98
The Order Recursive Versions of the Fast QRD Algorithmsp. 100
Conclusionp. 104
p. 105
p. 107
p. 111
Referencesp. 113
QRD Least-Squares Lattice Algorithmsp. 115
Fundamentals of QRD-LSL Algorithmsp. 116
LSL Interpolator and LSL Predictorp. 118
LSL interpolatorp. 119
Orthogonal bases for LSL interpolatorp. 121
LSL predictorp. 122
SRF Givens Rotation with Feedback Mechanismp. 123
SRF QRD-LSL Algorithmsp. 125
QRD based on interpolationp. 126
SRF QRD-LSL interpolation algorithmp. 129
SRF QRD-LSL prediction algorithm and SRF joint process estimationp. 136
SRF (QRD-LSL)-Based RLS Algorithmp. 139
Simulationsp. 140
Conclusionp. 142
Referencesp. 143
Multichannel Fast QRD-RLS Algorithmsp. 147
Introductionp. 147
Problem Formulationp. 149
Redefining the input vectorp. 151
Input vector for sequential-type multichannel algorithmsp. 152
Input vector for block-type multichannel algorithmsp. 153
Sequential-Type MC-FQRD-RLS Algorithmsp. 153
Triangularization of the information matrixp. 154
A priori and A posteriori versionsp. 157
Alternative implementationsp. 159
Block-Type MC-FQRD-RLS Algorithmsp. 162
The backward and forward prediction problemsp. 162
A priori and A posteriori versionsp. 166
Alternative implementationsp. 169
Order-Recursive MC-FQRD-RLS Algorithmsp. 171
Application Example and Computational Complexity Issuesp. 176
Application examplep. 176
Computational complexity issuesp. 178
Conclusionp. 179
Referencesp. 179
Householder-Based RLS Algorithmsp. 181
Householder Transformsp. 181
Hyperbolic Householder transformsp. 184
Row Householder transformsp. 184
The Householder RLS (HRLS) Algorithmp. 186
Applicationsp. 190
The Householder Block Exact QRD-RLS Algorithmp. 192
The Householder Block Exact Inverse QRD-RLS Algorithmp. 196
Sliding Window (SW) Householder Block Implementationp. 199
Conclusionp. 202
Referencesp. 202
Numerical Stability Propertiesp. 205
Introductionp. 205
Preliminariesp. 206
Conditioning, forward stability, and backward stabilityp. 208
The Conditioning of the Least-Squares Problemp. 210
The conditioning of the least-squares problemp. 211
Consistency, stability, and convergencep. 212
The Recursive QR Least-Squares Methodsp. 214
Full QR decomposition adaptive algorithmp. 214
Fast QR Algorithmsp. 220
Past input reconstructionp. 223
Reachable states in fast least-squares algorithmsp. 227
QR decomposition lattice algorithmp. 229
Conclusionp. 231
Referencesp. 232
Finite and Infinite-Precision Properties of QRD-RLS Algorithmsp. 235
Introductionp. 235
Precision Analysis of the QR-Decomposition RLS Algorithmp. 236
Infinite-precision analysisp. 237
Stability analysisp. 242
Error propagation analysis in steady-statep. 244
Simulation resultsp. 255
Precision Analysis of the Fast QRD-Lattice Algorithmp. 256
Infinite-precision analysisp. 258
Finite-precision analysisp. 261
Simulation resultsp. 265
Conclusionp. 266
Referencesp. 266
On Pipelined Implementations of QRD-RLS Adaptive Filtersp. 269
QRD-RLS Systolic Architecturep. 270
The Annihilation-Reording Look-Ahead Techniquep. 273
Look-ahead through bloack processingp. 274
Look-ahead through iterationp. 276
Relationship with multiply-and look-aheadp. 277
Parallelism in annihilation-recording look-aheadp. 279
Pipelined and block processing implementationsp. 280
Invariance of bounded input and bounded outputp. 283
Pipelined CORDIC-Based RLS Adaptive Filtersp. 283
Pipelined QRD-RLS with implicit weight extractionp. 284
Stability analysisp. 286
Pipelined QRD-RLS with explicit weight extractionp. 288
Conclusionp. 291
Appendixp. 294
Referencesp. 296
Weight Extraction of Fast QRD-RLS Algorithmsp. 299
FQRD-RLS Preliminariesp. 300
QR decomposition algorithmsp. 300
FQR_POS_B algorithmp. 301
System Identification with FQRD-RLSp. 303
Weight extraction in the FQRD-RLS algorithmp. 304
Examplep. 306
Burst-trained Equalizer with FQRD-RLSp. 308
Problem descriptionp. 309
Equvalent-output filteringp. 309
Equivalent-output filtering with explicit weight extractionp. 311
Examplep. 313
Active Noise Control and FQRD-RLSp. 314
Filtered-s RLSp. 315
Modified filtered-x FQRD-RLSp. 316
Examplep. 319
Multichannel and Lattice Implementationsp. 320
Conclusionp. 320
Referencesp. 321
On Linearly Constrained QRD-Based Algorithmsp. 323
Introductionp. 323
Optimal Linearly Constrained QRD-LS Filterp. 325
The Adaptive LC-IQRD-RLS Filtering Algorithmp. 327
The Adaptive GSC-IQRD-RLS Algorithmp. 331
Applicationsp. 335
Application 1: Adaptive LCMV filtering for spectrum estimationp. 335
Application 2: Adaptive LCMV antenna array beamformerp. 338
Conclusionp. 343
Referencesp. 343
Indexp. 347
Table of Contents provided by Ingram. All Rights Reserved.

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