This book presents the first comprehensive treatment of neural networks from an engineering perspective. Thorough, well-organized, and completely up-to-date, it examines all the important aspects of this emerging technology.

Neural Networks: A Comprehensive Foundation

Computers & Technology

SIMON HAYKIN

Certification

Macmillan Coll Div

Modern Filters

Engineering & Transportation

Engineering

Prentice Hall International Paperback Editions

This book develops the mathematical theory of linear adaptive filters with finite impulse response. Examples and computer experiment applications illustrate the theory and principles. The second edition has also been restructured with an introduction followed by four parts: discrete-time wide-sense station stochastic process; linear optimum filtering; linear FIR adaptive filtering; limitations, extensions and discussions. New features includes new chapters on QR decomposition-based lattice filters, on blind deconvolution, new appendix material on complex variables and regulation.

Adaptive Filter Theory (Prentice-Hall Information and System Sciences Series)

PRENTICE/HALL

Adaptive Filter Theory

Pearson/Education

Modern Wireless Communication

Michael Moher

Neural networks

Upper Saddle River, N.J. : Prentice Hall, c1999

Pearson Education Limited

This best-selling, easy to read book offers the most complete discussion on the theories and principles behind today's most advanced communications systems. Throughout, Haykin emphasizes the statistical underpinnings of communication theory in a complete and detailed manner. Readers are guided though topics ranging from pulse modulation and passband digital transmission to random processes and error-control coding. The fifth edition has also been revised to include an extensive treatment of digital communications.

Communication Systems 5/E

John Wiley&Sons

Leading experts present the latest research results in adaptive signal processing Recent developments in signal processing have made it clear that significant performance gains can be achieved beyond those achievable using standard adaptive filtering approaches. Adaptive Signal Processing presents the next generation of algorithms that will produce these desired results, with an emphasis on important applications and theoretical advancements. This highly unique resource brings together leading authorities in the field writing on the key topics of significance, each at the cutting edge of its own area of specialty. It begins by addressing the problem of optimization in the complex domain, fully developing a framework that enables taking full advantage of the power of complex-valued processing. Then, the challenges of multichannel processing of complex-valued signals are explored. This comprehensive volume goes on to cover Turbo processing, tracking in the subspace domain, nonlinear sequential state estimation, and speech-bandwidth extension. <ul>
<li> Examines the seven most important topics in adaptive filtering that will define the next-generation adaptive filtering solutions </li> <li> Introduces the powerful adaptive signal processing methods developed within the last ten years to account for the characteristics of real-life data: non-Gaussianity, non-circularity, non-stationarity, and non-linearity </li> <li> Features self-contained chapters, numerous examples to clarify concepts, and end-of-chapter problems to reinforce understanding of the material </li> <li> Contains contributions from acknowledged leaders in the field </li> </ul>
Adaptive Signal Processing is an invaluable tool for graduate students, researchers, and practitioners working in the areas of signal processing, communications, controls, radar, sonar, and biomedical engineering.

Adaptive Signal Processing: Next Generation Solutions

Tülay Adali

Networking & Cloud Computing

Wiley-IEEE Press

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. <ul>
<li> 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 </li> <li> Presents a powerful model-selection method called maximum marginal likelihood </li> <li> Addresses the principal bottleneck of kernel adaptive filters―their growing structure </li> <li> Features twelve computer-oriented experiments to reinforce the concepts, with MATLAB codes downloadable from the authors' Web site </li> <li> Concludes each chapter with a summary of the state of the art and potential future directions for original research </li> </ul>
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.

Kernel Adaptive Filtering: A Comprehensive Introduction

Weifeng Liu

José C. Principe

Computer Science

wiley

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