Adaptive Filter Theory, 5/e (IE-Paperback)

Simon Haykin

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  • <內容簡介>
    Preface
  • Acknowledgments
  • Background and Preview
  • 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
  • Epilogue
  • Appendix A  Theory of Complex Variables
  • Appendix B  Computation of Derivatives in the Complex Domain
  • Appendix C  Method of Lagrange Multipliers
  • Appendix D  Estimation Theory
  • Appendix E  Eigenanalysis
  • Appendix F  Langevin Equation of Nonequilibrium Thermodynamics
  • Appendix G  Rotations and Reflections
  • Appendix H  Complex Wishart Distribution