Chapter 1. Vectors -- chapter 2. Matrices -- chapter 3. Processing of discrete deterministic signals : discrete systems -- chapter 4. Discrete-time random processes -- chapter 5. The Wiener filter -- chapter 6. Eigenvalues of Rx : properties of the error surface -- chapter 7. Newton's and steepest descent methods -- chapter 8. The least mean-square algorithm -- chapter 9. Variants of least mean-square algorithm
Summary
Adaptive filters are used in many diverse applications, appearing in everything from military instruments to cellphones and home appliances. Adaptive Filtering: Fundamentals of Least Mean Squares with MATLAB® covers the core concepts of this important field, focusing on a vital part of the statistical signal processing area-the least mean square (LMS) adaptive filter. This largely self-contained text:Discusses random variables, stochastic processes, vectors, matrices, determinants, discrete random signals, and probability distributionsExplains how to find the eigenvalues and eigenvectors of a
