Adaptive Filter Theory
Seiten
1995
|
3rd Revised edition
Pearson Education (US) (Verlag)
978-0-13-322760-4 (ISBN)
Pearson Education (US) (Verlag)
978-0-13-322760-4 (ISBN)
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Examines both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR), and the elements of supervised neural networks. For use on undergraduate courses in adaptive signal processing, this edition has been updated and refined.
Haykin examines both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. This edition has been updated and refined to keep current with the field and develop concepts in as unified and accessible a manner as possible. It: introduces a completely new chapter on Frequency-Domain Adaptive Filters; adds a chapter on Tracking Time-Varying Systems; adds two chapters on Neural Networks; enhances material on RLS algorithms; strengthens linkages to Kalman filter theory to gain a more unified treatment of the standard, square-root and order-recursive forms; and includes new computer experiments using MATLAB software that illustrate the underlying theory and applications of the LMS and RLS algorithms.
Haykin examines both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. This edition has been updated and refined to keep current with the field and develop concepts in as unified and accessible a manner as possible. It: introduces a completely new chapter on Frequency-Domain Adaptive Filters; adds a chapter on Tracking Time-Varying Systems; adds two chapters on Neural Networks; enhances material on RLS algorithms; strengthens linkages to Kalman filter theory to gain a more unified treatment of the standard, square-root and order-recursive forms; and includes new computer experiments using MATLAB software that illustrate the underlying theory and applications of the LMS and RLS algorithms.
BACKGROUND MATERIAL. Discrete-Time Signal Processing. Stationary Processes and Models. Spectrum Analysis. Eigenanalysis. LINEAR OPTIMUM FILTERING. Wiener Filters. Linear Prediction. Kalman Filters. LINEAR ADAPTIVE FILTERING. Method of Steepest Descent. Least-Mean Square Algorithm. Frequency-Domain Adaptive Filters. Method of Least Squares. Rotations and Reflections. Recursive Least-Squares Algorithm. Square-Root Adaptive Filtering. Order-Recursive Adaptive Filters. Tracking of Time-Varying Systems. Finite-Precision Effects. NONLINEAR ADAPTIVE FILTERING. Blind Deconvolution. Back-Propagation Learning. Radial Basis Function Networks.
Zusatzinfo | Illustrations |
---|---|
Verlagsort | Upper Saddle River |
Sprache | englisch |
Maße | 178 x 235 mm |
Gewicht | 1582 g |
Themenwelt | Informatik ► Grafik / Design ► Digitale Bildverarbeitung |
Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
Mathematik / Informatik ► Mathematik | |
Technik ► Elektrotechnik / Energietechnik | |
Technik ► Maschinenbau | |
ISBN-10 | 0-13-322760-X / 013322760X |
ISBN-13 | 978-0-13-322760-4 / 9780133227604 |
Zustand | Neuware |
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