Adaptive filter theory
著者
書誌事項
Adaptive filter theory
（Always learning）
Pearson, c2014
5th ed., International ed
- : pbk
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注記
Includes bibliographical references (p. 864-878) and index
内容説明・目次
内容説明
<> Adaptive Filter Theory, 5e, is ideal for courses in Adaptive Filters.
Haykin examines both the mathematical theory behind various linear adaptive filters and the elements of supervised multilayer perceptrons. In its fifth edition, this highly successful book has been updated and refined to stay current with the field and develop concepts in as unified and accessible a manner as possible.
目次
Chapter 1 Stochastic Processes and Models
Chapter 2 Wiener Filters
Chapter 3 Linear Prediction
Chapter 4 Method of Steepest Descent
Chapter 5 Method of Stochastic Gradient Descent
Chapter 6 The Least-Mean-Square (LMS) Algorithm
Chapter 7 Normalized Least-Mean-Square (LMS) Algorithm and Its Generalization
Chapter 8 Block-Adaptive Filters
Chapter 9 Method of Least Squares
Chapter 10 The Recursive Least-Squares (RLS) Algorithm
Chapter 11 Robustness
Chapter 12 Finite-Precision Effects
Chapter 13 Adaptation in Nonstationary Environments
Chapter 14 Kalman Filters
Chapter 15 Square-Root Adaptive Filters
Chapter 16 Order-Recursive Adaptive Filters
Chapter 17 Blind Deconvolution
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