Adaptive Filtering Primer with MATLAB

Introduction to: Discrete-time signal processing; Random variables, sequences and stochastic processes; Wiener filters; Eigenvalue 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.

Adaptive Filtering Primer with MATLAB

Poularikas, Alexander D., Ramadan, Zayed M.

CRC Press.

2017

Abstract

Adaptive Filtering Primer with MATLAB clearly explains the fundamentals of adaptive filtering supported by numerous examples and computer simulations. 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 completes the self-contained coverage.

Introduction to: Discrete-time signal processing; Random variables, sequences and stochastic processes; Wiener filters; Eigenvalue 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.

Citation

Poularikas, Alexander D., Ramadan, Zayed M. Adaptive Filtering Primer with MATLAB. CRC Press, 2017.

Collection

Ngành CÔNG NGHỆ KỸ THUẬT MÁY TÍNH (7480108)

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