Adaptive Filter Theory looks at both the mathematical theory behind various linear adaptive filters with finite-duration impulse response (FIR) and the elements of supervised neural networks. Up-to-date and in-depth treatment of adaptive filters develops concepts in a unified and accessible manner.
This highly successful book provides comprehensive coverage of adaptive filters in a highly readable and understandable fashion. Includes an extensive use of illustrative examples; and MATLAB experiments, which illustrate the practical realities and intricacies of adaptive filters, the codes for which can be downloaded from the Web. Covers a wide range of topics including Stochastic Processes, Wiener Filters, and Kalman Filters.
For those interested in learning about adaptive filters and the theories behind them.
At a level suitable for graduate courses on adaptive signal processing, this textbook develops the mathematical theory of various realizations of linear adaptive filters with finite-duration impulse response, and also provides an introductory treatment of supervised neural networks. Numerous computer experiments illustrate the underlying theory and applications of the LMS (least mean-square) and RLS (recursive-least-squares) algorithms, and problems conclude each chapter. Annotation c. Book News, Inc., Portland, OR (booknews.com)