Alexander D. Poularikas

Adaptive Filtering Primer with MATLAB (eBook, PDF)

• Format: PDF

Produktbeschreibung

Adaptive Filtering Primer with MATLAB® clearly explains the fundamentals of adaptive filtering supported by practical examples and computer experiments and functions. The authors introduce discrete-time signal processing, random variables and stochastic processes, the Wiener filter, properties of the error surface, the steepest descent method, and the least mean square (LMS) algorithm. They also supply many MATLAB® functions and m-files along with computer experiments to illustrate how to apply the concepts to real-world problems. The book includes problems along with hints, suggestions, and solutions for solving them. An appendix on matrix computations rounds out the self-contained coverage.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 240
• Erscheinungstermin: 19. Dezember 2017
• Englisch
• ISBN-13: 9781420006384
• Artikelnr.: 53366585

Autorenporträt

Alexander D. Poularikas, Zayed M. Ramadan

Inhaltsangabe

Introduction. Discrete
Time Signal Processing. Random Variables, Sequences, and Stochastic Processes. Wiener Filters. Eigenvalues of Rx
Properties of the Error Surface. Newton and Steepest
Descent Method. The Least Mean
Square (LMS) Algorithm. Variations of LMS Algorithms. Least Squares and Recursive Least
Squares Signal Processing. Abbreviations. Bibliography. Appendix A: Matrix Analysis. Index.