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poularikas alexander d.; ramadan zayed m. - adaptive filtering primer with matlab

Adaptive Filtering Primer with MATLAB

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Dettagli

Genere:Libro
Lingua: Inglese
Editore:

CRC Press

Pubblicazione: 02/2006
Edizione: 1° edizione





Trama

Adaptive Filtering Primer with MATLAB clearly explains the fundamentals of adaptive filtering supported by practical examples and computer experiments and functions. 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 rounds out the self-contained coverage.




Note Editore

Because of the wide use of adaptive filtering in digital signal processing and, because most of the modern electronic devices include some type of an adaptive filter, a text that brings forth the fundamentals of this field was necessary. The material and the principles presented in this book are easily accessible to engineers, scientists, and students who would like to learn the fundamentals of this field and have a background at the bachelor level.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.With applications across a wide range of areas, including radar, communications, control, medical instrumentation, and seismology, Adaptive Filtering Primer with MATLAB® is an ideal companion for quick reference and a perfect, concise introduction to the field.




Sommario

INTRODUCTIONSignal ProcessingAn ExampleOutline of the TextDISCRETE-TIME SIGNAL PROCESSINGDiscrete Time SignalsTransform-Domain Representation of Discrete-Time SignalsThe Z-TransformDiscrete-Time SystemsProblemsHints-Solutions-SuggestionsRANDOM VARIABLES, SEQUENCES, AND STOCHASTIC PROCESSESRandom Signals and DistributionsAveragesStationary ProcessesSpecial Random Signals and Probability Density FunctionsWiener-Khinchin RelationsFiltering Random ProcessesSpecial Types of Random ProcessesNonparametric Spectra EstimationParametric Methods of power Spectral EstimationProblemsHints-Solutions-SuggestionsWIENER FILTERSThe Mean-Square ErrorThe FIR Wiener FilterThe Wiener SolutionWiener Filtering ExamplesProblemsHints-Solutions-SuggestionsEIGENVALUES OF RX - PROPERTIES OF THE ERROR SURFACEThe Eigenvalues of the Correlation MatrixGeometrical Properties of the Error SurfaceProblemsHints-Solutions-SuggestionsNEWTON AND STEEPEST-DESCENT METHODOne-Dimensional Gradient Search MethodSteepest-Descent AlgorithmProblemsHints-Solutions-SuggestionsTHE LEAST MEAN-SQUARE (LMS) ALGORITHMIntroductionDerivation of the LMS AlgorithmExamples Using the LMS Algorithm EquationPerformance Analysis of the LMS Algorithm EquationLearning CurveComplex Representation of LMS AlgorithmProblemsHints-Solutions-SuggestionsVARIATIONS OF LMS ALGORITHMSThe Sign AlgorithmsNormalized LMS (NLMS) AlgorithmVariable Step-Size LMS (VSLMS) AlgorithmThe Leaky LMS AlgorithmLinearly Constrained LMS AlgorithmSelf-Correcting Adaptive Filtering (SCAF)Transform Domain Adaptive LMS FilteringError Normalized LMS AlgorithmsProblemsHints-Solutions-SuggestionsLEAST SQUARES AND RECURSIVE LEAST-SQUARES SIGNAL PROCESSINGIntroduction to Least SquaresLeast-Square FormulationLeast-Squares ApproachOrthogonality PrincipleProjection OperatorLeast-Squares Finite Impulse Response FilterIntroduction to RLS AlgorithmProblemsHints-Solutions-SuggestionsABBREVIATIONSBIBLIOGRAPHYAPPENDIX A: MATRIX ANALYSISINDEX










Altre Informazioni

ISBN:

9780849370434

Condizione: Nuovo
Collana: Electrical Engineering Primer Series
Dimensioni: 9.25 x 6.25 in Ø 0.70 lb
Formato: Brossura
Illustration Notes:78 b/w images and 11 tables
Pagine Arabe: 238


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