Ebook: Adaptive Filtering: Algorithms and Practical Implementation

In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation.

The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes:

• Analytical and simulation examples in Chapters 4, 5, 6 and 10
  
• Appendix E, which summarizes the analysis of set-membership algorithm
  
• Updated problems and references
  

Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more.

Several problems are included at the end of chapters, and some of these problems address applications. A user-friendly MATLAB package is provided where the reader can easily solve new problems and test algorithms in a quick manner. Additionally, the book provides easy access to working algorithms for practicing engineers.

In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation.

The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes:

• Analytical and simulation examples in Chapters 4, 5, 6 and 10
  
• Appendix E, which summarizes the analysis of set-membership algorithm
  
• Updated problems and references
  

Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more.

Several problems are included at the end of chapters, and some of these problems address applications. A user-friendly MATLAB package is provided where the reader can easily solve new problems and test algorithms in a quick manner. Additionally, the book provides easy access to working algorithms for practicing engineers.

In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation.

The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes:

• Analytical and simulation examples in Chapters 4, 5, 6 and 10
  
• Appendix E, which summarizes the analysis of set-membership algorithm
  
• Updated problems and references
  

Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more.

Several problems are included at the end of chapters, and some of these problems address applications. A user-friendly MATLAB package is provided where the reader can easily solve new problems and test algorithms in a quick manner. Additionally, the book provides easy access to working algorithms for practicing engineers.

Content:
Front Matter....Pages i-xxi
Introduction to Adaptive Filtering....Pages 1-11
Fundamentals of Adaptive Filtering....Pages 13-78
The Least-Mean-Square (LMS) Algorithm....Pages 79-135
LMS-Based Algorithms....Pages 137-207
Conventional RLS Adaptive Filter....Pages 209-247
Data-Selective Adaptive Filtering....Pages 249-304
Adaptive Lattice-Based RLS Algorithms....Pages 305-347
Fast Transversal RLS Algorithms....Pages 349-365
QR-Decomposition-Based RLS Filters....Pages 367-409
Adaptive IIR Filters....Pages 411-466
Nonlinear Adaptive Filtering....Pages 467-499
Subband Adaptive Filters....Pages 501-549
Blind Adaptive Filtering....Pages 551-583
Complex Differentiation....Pages 585-590
Quantization Effects in the LMS Algorithm....Pages 591-603
Quantization Effects in the RLS Algorithm....Pages 605-621
Kalman Filters....Pages 623-634
Analysis of Set-Membership Affine Projection Algorithm....Pages 635-641
Back Matter....Pages 653-652

In the fourth edition of Adaptive Filtering: Algorithms and Practical Implementation, author Paulo S.R. Diniz presents the basic concepts of adaptive signal processing and adaptive filtering in a concise and straightforward manner. The main classes of adaptive filtering algorithms are presented in a unified framework, using clear notations that facilitate actual implementation.

The main algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Many examples address problems drawn from actual applications. New material to this edition includes:

• Analytical and simulation examples in Chapters 4, 5, 6 and 10
  
• Appendix E, which summarizes the analysis of set-membership algorithm
  
• Updated problems and references
  

Providing a concise background on adaptive filtering, this book covers the family of LMS, affine projection, RLS and data-selective set-membership algorithms as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more.

Several problems are included at the end of chapters, and some of these problems address applications. A user-friendly MATLAB package is provided where the reader can easily solve new problems and test algorithms in a quick manner. Additionally, the book provides easy access to working algorithms for practicing engineers.

Content:
Front Matter....Pages i-xxi
Introduction to Adaptive Filtering....Pages 1-11
Fundamentals of Adaptive Filtering....Pages 13-78
The Least-Mean-Square (LMS) Algorithm....Pages 79-135
LMS-Based Algorithms....Pages 137-207
Conventional RLS Adaptive Filter....Pages 209-247
Data-Selective Adaptive Filtering....Pages 249-304
Adaptive Lattice-Based RLS Algorithms....Pages 305-347
Fast Transversal RLS Algorithms....Pages 349-365
QR-Decomposition-Based RLS Filters....Pages 367-409
Adaptive IIR Filters....Pages 411-466
Nonlinear Adaptive Filtering....Pages 467-499
Subband Adaptive Filters....Pages 501-549
Blind Adaptive Filtering....Pages 551-583
Complex Differentiation....Pages 585-590
Quantization Effects in the LMS Algorithm....Pages 591-603
Quantization Effects in the RLS Algorithm....Pages 605-621
Kalman Filters....Pages 623-634
Analysis of Set-Membership Affine Projection Algorithm....Pages 635-641
Back Matter....Pages 653-652
....