This book is based on a graduate level course offered by the author at UCLA and has been classed tested there and at other universities over a number of years. This will be the most comprehensive book on the market today providing instructors a wide choice in designing their courses. Offers computer problems to illustrate real life applications for students and professionals alike An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department. An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.
ALI H. SAYED, PhD, is a professor of electrical engineering at UCLA, where he established and directs the Adaptive Systems Laboratory. He is a Fellow of the IEEE for his contributions to adaptive filtering and estimation algorithms.
Preface. Acknowledgments. Notation. Symbols. Optimal Estimation. Linear Estimation. Constrained Linear Estimation. Steepest-Descent Algorithms. Stochastic-Gradient Algorithms. Steady-State Performance of Adaptive Filters. Tracking Performance of Adaptive Filters. Finite Precision Effects. Transient Performance of Adaptive Filters. Block Adaptive Filters. The Least-Squares Criterion. Recursive Least-Squares. RLS Array Algorithms. Fast Fixed-Order Filters. Lattice Filters. Laguerre Adaptive Filters. Robust Adaptive Filters. Bibliography. Author Index. Subject Index. AC