Published by Pearson (May 28, 2014) © 2014
Simon HaykinFor 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.
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Chapter 1 Stochastic Processes and Models
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Chapter 2 Wiener Filters
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Chapter 3 Linear Prediction
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Chapter 4 Method of Steepest Descent
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Chapter 5 Method of Stochastic Gradient Descent
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Chapter 6 The Least-Mean-Square (LMS) Algorithm
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Chapter 7 Normalized Least-Mean-Square (LMS) Algorithm and Its Generalization
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Chapter 8 Block-Adaptive Filters
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Chapter 9 Method of Least Squares
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Chapter 10 The Recursive Least-Squares (RLS) Algorithm
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Chapter 11 Robustness
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Chapter 12 Finite-Precision Effects
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Chapter 13 Adaptation in Nonstationary Environments
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Chapter 14 Kalman Filters
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Chapter 15 Square-Root Adaptive Filters
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Chapter 16 Order-Recursive Adaptive Filters
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Chapter 17 Blind Deconvolution