Solution Manual for Adaptive Filter Theory 5th Edition by Haykin

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Solution Manual for Adaptive Filter Theory 5th Edition by Haykin

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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 Filtering Algorithms

Chapter 16: Order-Recursive Adaptive Filtering Algorithm

Chapter 17: Blind Deconvolution

Appendix

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