Adaptive Filter Theory; Simon Haykin; 2001

Adaptive Filter Theory Upplaga 4

av Simon Haykin
CONTENTS

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 Least-Mean-Square Adaptive Filters Chapter 6 Normalized Least-Mean-Square Adaptive Filters Chapter 7 Frequency-Domain and Subband Adaptive Filters Chapter 8 Method of Least Squares Chapter 9 Recursive Least-Square Adaptive Filters Chapter 10 Kalman Filters Chapter 11 Square-Root Adaptive Filters Chapter 12 Order-Recursive Adaptive Filters Chapter 13 Finite-Precision Effects Chapter 14 Tracking of Time-Varying Systems Chapter 15 Adaptive Filters Using Infinite-Duration Impulse Response Structures Chapter 16 Blind Deconvolution Chapter 17 Back-Propagation Learning Epilogue

Appendix A Complex Variables Appendix B Differentiation with Respect to a Vector Appendix C Method of Lagrange Multipliers Appendix D Estimation Theory Appendix E Eigenanalysis Appendix F Rotations and Reflections Appendix G Complex Wishart Distribution Glossary Bibliography Index
CONTENTS

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 Least-Mean-Square Adaptive Filters Chapter 6 Normalized Least-Mean-Square Adaptive Filters Chapter 7 Frequency-Domain and Subband Adaptive Filters Chapter 8 Method of Least Squares Chapter 9 Recursive Least-Square Adaptive Filters Chapter 10 Kalman Filters Chapter 11 Square-Root Adaptive Filters Chapter 12 Order-Recursive Adaptive Filters Chapter 13 Finite-Precision Effects Chapter 14 Tracking of Time-Varying Systems Chapter 15 Adaptive Filters Using Infinite-Duration Impulse Response Structures Chapter 16 Blind Deconvolution Chapter 17 Back-Propagation Learning Epilogue

Appendix A Complex Variables Appendix B Differentiation with Respect to a Vector Appendix C Method of Lagrange Multipliers Appendix D Estimation Theory Appendix E Eigenanalysis Appendix F Rotations and Reflections Appendix G Complex Wishart Distribution Glossary Bibliography Index
Upplaga: 4e upplagan
Utgiven: 2001
ISBN: 9780130484345
Förlag: Pearson Education
Format: Bok
Språk: Engelska
Sidor: 936 st
CONTENTS

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 Least-Mean-Square Adaptive Filters Chapter 6 Normalized Least-Mean-Square Adaptive Filters Chapter 7 Frequency-Domain and Subband Adaptive Filters Chapter 8 Method of Least Squares Chapter 9 Recursive Least-Square Adaptive Filters Chapter 10 Kalman Filters Chapter 11 Square-Root Adaptive Filters Chapter 12 Order-Recursive Adaptive Filters Chapter 13 Finite-Precision Effects Chapter 14 Tracking of Time-Varying Systems Chapter 15 Adaptive Filters Using Infinite-Duration Impulse Response Structures Chapter 16 Blind Deconvolution Chapter 17 Back-Propagation Learning Epilogue

Appendix A Complex Variables Appendix B Differentiation with Respect to a Vector Appendix C Method of Lagrange Multipliers Appendix D Estimation Theory Appendix E Eigenanalysis Appendix F Rotations and Reflections Appendix G Complex Wishart Distribution Glossary Bibliography Index
CONTENTS

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 Least-Mean-Square Adaptive Filters Chapter 6 Normalized Least-Mean-Square Adaptive Filters Chapter 7 Frequency-Domain and Subband Adaptive Filters Chapter 8 Method of Least Squares Chapter 9 Recursive Least-Square Adaptive Filters Chapter 10 Kalman Filters Chapter 11 Square-Root Adaptive Filters Chapter 12 Order-Recursive Adaptive Filters Chapter 13 Finite-Precision Effects Chapter 14 Tracking of Time-Varying Systems Chapter 15 Adaptive Filters Using Infinite-Duration Impulse Response Structures Chapter 16 Blind Deconvolution Chapter 17 Back-Propagation Learning Epilogue

Appendix A Complex Variables Appendix B Differentiation with Respect to a Vector Appendix C Method of Lagrange Multipliers Appendix D Estimation Theory Appendix E Eigenanalysis Appendix F Rotations and Reflections Appendix G Complex Wishart Distribution Glossary Bibliography Index
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I december 2001 släpptes boken Adaptive Filter Theory skriven av Simon Haykin. Det är den 4e upplagan av kursboken. Den är skriven på engelska och består av 936 sidor djupgående information om övrigt. Förlaget bakom boken är Pearson Education.

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