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A Rapid Introduction to Adaptive Filtering (SpringerBriefs in Electrical and Computer Engineering) - Softcover

Book 23 of 209: SpringerBriefs in Electrical and Computer Engineering

Vega, Leonardo Rey; Rey, Hernan

 
9783642302985: A Rapid Introduction to Adaptive Filtering (SpringerBriefs in Electrical and Computer Engineering)
Softcover
ISBN 10: 364230298X ISBN 13: 9783642302985
Publisher: Springer, 2012

Synopsis

In this book, the authors provide insights into the basics of adaptive filtering, which are particularly useful for students taking their first steps into this field. They start by studying the problem of minimum mean-square-error filtering, i.e., Wiener filtering. Then, they analyze iterative methods for solving the optimization problem, e.g., the Method of Steepest Descent. By proposing stochastic approximations, several basic adaptive algorithms are derived, including Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS) and Sign-error algorithms. The authors provide a general framework to study the stability and steady-state performance of these algorithms. The affine Projection Algorithm (APA) which provides faster convergence at the expense of computational complexity (although fast implementations can be used) is also presented. In addition, the Least Squares (LS) method and its recursive version (RLS), including fast implementations are discussed. The book closes withthe discussion of several topics of interest in the adaptive filtering field.

"synopsis" may belong to another edition of this title.

From the Back Cover

In this book, the authors provide insights into the basics of adaptive filtering, which are particularly useful for students taking their first steps into this field. They start by studying the problem of minimum mean-square-error filtering, i.e., Wiener filtering. Then, they analyze iterative methods for solving the optimization problem, e.g., the Method of Steepest Descent. By proposing stochastic approximations, several basic adaptive algorithms are derived, including Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS) and Sign-error algorithms. The authors provide a general framework to study the stability and steady-state performance of these algorithms. The affine Projection Algorithm (APA) which provides faster convergence at the expense of computational complexity (although fast implementations can be used) is also presented. In addition, the Least Squares (LS) method and its recursive version (RLS), including fast implementations are discussed. The book closes withthe discussion of several topics of interest in the adaptive filtering field.

"About this title" may belong to another edition of this title.

Publisher
Springer
Publication date
2012
Language
English
ISBN 10 
364230298X
ISBN 13 
9783642302985
Binding
Paperback
Number of pages
134