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Informationen zum Autor Ali H. Sayed is 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. His research has attracted several recognitions including the 2003 Kuwait Prize, 2005 Terman Award, and several IEEE Best Paper Awards. Klappentext Adaptive FiltersAdaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. Now, preserving the style and main features of the earlier award-winning publication, Fundamentals of Adaptive Filtering (2005 Terman Award), the author offers readers and instructors a concentrated, systematic, and up-to-date treatment of the subject in this valuable new book.Adaptive Filters allows readers to gain a gradual and solid introduction to the subject, its applications to a variety of topical problems, existing limitations, and extensions of current theories. The book consists of eleven parts-each part containing a series of focused lectures and ending with bibliographic comments, problems, and computer projects with MATLAB(r) solutions available to all readers. Additional features include:*Numerous tables, figures, and projects*Special focus on geometric constructions, physical intuition, linear-algebraic concepts, and vector notation*Background material on random variables, linear algebra, and complex gradients collected in three introductory chapters*Complete solutions manual available for instructors*MATLAB(r) solutions available for all computer projectsAdaptive Filters offers a fresh, focused look at the subject in a manner that will entice students, challenge experts, and appeal to practitioners and instructors. Zusammenfassung The textbook provides a comprehensive, thorough, and up-to-date treatment of adaptive Includes solved practical computer projects that illustrate how the material developed in the textbook can be used to solve problems of practical relevance. Inhaltsverzeichnis Preface and Acknowledgments. Notation and Symbols. BACKGROUND MATERIAL. A. Random Variables. A.1 Variance of a Random Variable. A.2 Dependent Random Variables. A.3 Complex-Valued Random Variables. A.4 Vector-Valued Random Variables. A.5 Gaussian Random Vectors. B. Linear Algebra. B.1 Hermitian and Positive-Definite Matrices. B.2 Range Spaces and Nullspaces of Matrices. B.3 Schur Complements. B.4 Cholesky Factorization. B.5 QR Decomposition. B.6 Singular Value Decomposition. B.7 Kronecker Products. C. Complex Gradients. C.1 Cauchy-Riemann Conditions. C.2 Scalar Arguments. C.3 Vector Arguments. PART I: OPTIMAL ESTIMATION. 1. Scalar-Valued Data. 1.1 Estimation Without Observations. 1.2 Estimation Given Dependent Observations. 1.3 Orthogonality Principle. 1.4 Gaussian Random Variables. 2. Vector-Valued Data. 2.1 Optimal Estimator in the Vector Case. 2.2 Spherically Invariant Gaussian Variables. 2.3 Equivalent Optimization Criterion. Summary and Notes. Problems and Computer Projects. PART II: LINEAR ESTIMATION. 3. Normal Equations. 3.1 Mean-Square Error Criterion. 3.2 Minimization by Differentiation. 3.3 Minimization by Completion-of-Squares. 3.4 Minimization of the Error Covariance Matrix. 3.5 Optimal Linear Estimator. 4. Orthogonality Principle. 4.1 Design Examples. 4.2 Orthogonality Condition. 4.3 Existence of Solutions. 4.4 Nonzero-Mean Variables. 5. Linear Models. 5.1 Estimation using Linear Rela...
