Adaptive Filter Theory

Author: Simon Haykin
Publisher: Prentice Hall
ISBN: 9780132671453
Format: PDF, Mobi
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Includes bibliographical references (pages 846-878) and index.

Adaptive Filter Theory International Edition

Author: Simon Haykin
Publisher: Pearson Higher Ed
ISBN: 0273775723
Format: PDF, Mobi
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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.

Adaptive Filter Theory

Author: Simon S. Haykin
Publisher:
ISBN: 9780130484345
Format: PDF
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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

Least Mean Square Adaptive Filters

Author: Simon Haykin
Publisher: John Wiley & Sons
ISBN: 9780471215707
Format: PDF, Kindle
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Edited by the original inventor of the technology. Includes contributions by the foremost experts in thefield. The only book to cover these topics together.

Linear least squares estimation

Author: Thomas Kailath
Publisher: Academic Pr
ISBN: 9780879330989
Format: PDF, ePub, Docs
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A survey of the field; Mathematical foundations of least-squares prediction theory; Wiener-hopf equations and optimum filters; State-space models and recursive filters.

Adaptive Filters

Author: Behrouz Farhang-Boroujeny
Publisher: John Wiley & Sons
ISBN: 111859133X
Format: PDF
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This second edition of Adaptive Filters: Theory and Applications has been updated throughout to reflect the latest developments in this field; notably an increased coverage given to the practical applications of the theory to illustrate the much broader range of adaptive filters applications developed in recent years. The book offers an easy to understand approach to the theory and application of adaptive filters by clearly illustrating how the theory explained in the early chapters of the book is modified for the various applications discussed in detail in later chapters. This integrated approach makes the book a valuable resource for graduate students; and the inclusion of more advanced applications including antenna arrays and wireless communications makes it a suitable technical reference for engineers, practitioners and researchers. Key features: • Offers a thorough treatment of the theory of adaptive signal processing; incorporating new material on transform domain, frequency domain, subband adaptive filters, acoustic echo cancellation and active noise control. • Provides an in-depth study of applications which now includes extensive coverage of OFDM, MIMO and smart antennas. • Contains exercises and computer simulation problems at the end of each chapter. • Includes a new companion website hosting MATLAB® simulation programs which complement the theoretical analyses, enabling the reader to gain an in-depth understanding of the behaviours and properties of the various adaptive algorithms.

Adaptive Filtering

Author: Paulo S.R. Diniz
Publisher: Springer Science & Business Media
ISBN: 1475736371
Format: PDF, Mobi
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Adaptive Filtering: Algorithms and Practical Implementation, Second Edition, presents a concise overview of adaptive filtering, covering as many algorithms as possible in a unified form that avoids repetition and simplifies notation. It is suitable as a textbook for senior undergraduate or first-year graduate courses in adaptive signal processing and adaptive filters. The philosophy of the presentation is to expose the material with a solid theoretical foundation, to concentrate on algorithms that really work in a finite-precision implementation, and to provide easy access to working algorithms. Hence, practicing engineers and scientists will also find the book to be an excellent reference. This second edition contains a substantial amount of new material: -Two new chapters on nonlinear and subband adaptive filtering; -Linearly constrained Weiner filters and LMS algorithms; -LMS algorithm behavior in fast adaptation; -Affine projection algorithms; -Derivation smoothing; -MATLAB codes for algorithms.

Kernel Adaptive Filtering

Author: Weifeng Liu
Publisher: John Wiley & Sons
ISBN: 1118211219
Format: PDF, Docs
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Online learning from a signal processing perspective There is increased interest in kernel learning algorithms in neural networks and a growing need for nonlinear adaptive algorithms in advanced signal processing, communications, and controls. Kernel Adaptive Filtering is the first book to present a comprehensive, unifying introduction to online learning algorithms in reproducing kernel Hilbert spaces. Based on research being conducted in the Computational Neuro-Engineering Laboratory at the University of Florida and in the Cognitive Systems Laboratory at McMaster University, Ontario, Canada, this unique resource elevates the adaptive filtering theory to a new level, presenting a new design methodology of nonlinear adaptive filters. Covers the kernel least mean squares algorithm, kernel affine projection algorithms, the kernel recursive least squares algorithm, the theory of Gaussian process regression, and the extended kernel recursive least squares algorithm Presents a powerful model-selection method called maximum marginal likelihood Addresses the principal bottleneck of kernel adaptive filters—their growing structure Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site Concludes each chapter with a summary of the state of the art and potential future directions for original research Kernel Adaptive Filtering is ideal for engineers, computer scientists, and graduate students interested in nonlinear adaptive systems for online applications (applications where the data stream arrives one sample at a time and incremental optimal solutions are desirable). It is also a useful guide for those who look for nonlinear adaptive filtering methodologies to solve practical problems.

Statistical and Adaptive Signal Processing

Author: Dimitris G. Manolakis
Publisher: Artech House on Demand
ISBN: 9781580536103
Format: PDF, ePub, Docs
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This authoritative volume on statistical and adaptive signal processing offers you a unified, comprehensive and practical treatment of spectral estimation, signal modeling, adaptive filtering, and array processing. Packed with over 3,000 equations and more than 300 illustrations, this unique resource provides you with balanced coverage of implementation issues, applications, and theory, making it a smart choice for professional engineers and students alike.