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Adaptive Filtering Primer with MATLAB [Pehme köide]

(The University of Alabama, Huntsville, USA)
  • Formaat: Paperback / softback, 238 pages, kõrgus x laius: 234x156 mm, kaal: 317 g, 11 Tables, black and white; 78 Illustrations, black and white
  • Sari: Electrical Engineering Primer Series
  • Ilmumisaeg: 14-Feb-2006
  • Kirjastus: CRC Press Inc
  • ISBN-10: 0849370434
  • ISBN-13: 9780849370434
  • Formaat: Paperback / softback, 238 pages, kõrgus x laius: 234x156 mm, kaal: 317 g, 11 Tables, black and white; 78 Illustrations, black and white
  • Sari: Electrical Engineering Primer Series
  • Ilmumisaeg: 14-Feb-2006
  • Kirjastus: CRC Press Inc
  • ISBN-10: 0849370434
  • ISBN-13: 9780849370434
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.
Introduction
1(4)
Signal processing
1(1)
An example
1(1)
Outline of the text
2(3)
Discrete-time signal processing
5(14)
Discrete-time signals
5(1)
Transform-domain representation of discrete-time signals
5(6)
The Z-Transform
11(2)
Discrete-time systems
13(6)
Problems
17(1)
Hints-solutions-suggestions
17(2)
Random variables, sequences, and stochastic processes
19(36)
Random signals and distributions
19(3)
Averages
22(4)
Stationary processes
26(3)
Special random signals and probability density functions
29(3)
Wiener-Khintchin relations
32(2)
Filtering random processes
34(2)
Special types of random processes
36(4)
Nonparametric spectra estimation
40(9)
Parametric methods of power spectral estimations
49(6)
Problems
51(1)
Hints-solutions-suggestions
52(3)
Wiener filters
55(22)
The mean-square error
55(1)
The FIR Wiener filter
55(4)
The Wiener solution
59(4)
Wiener filtering examples
63(14)
Problems
73(1)
Hints-solutions-suggestions
74(3)
Eigenvalues of Rx --- properties of the error surface
77(8)
The eigenvalues of the correlation matrix
77(2)
Geometrical properties of the error surface
79(6)
Problems
81(1)
Hints-solutions-suggestions
82(3)
Newton and steepest-descent method
85(16)
One-dimensional gradient search method
85(6)
Steepest-descent algorithm
91(10)
Problems
96(1)
Hints-solutions-suggestions
97(4)
The least mean-square (LMS) algorithm
101(36)
Introduction
101(1)
Derivation of the LMS algorithm
102(2)
Examples using the LMS algorithm
104(8)
Performance analysis of the LMS algorithm
112(14)
Complex representation of LMS algorithm
126(11)
Problems
129(1)
Hints-solutions-suggestions
130(7)
Variations of LMS algorithms
137(34)
The sign algorithms
137(2)
Normalized LMS (NLMS) algorithm
139(2)
Variable step-size LMS (VSLMS) algorithm
141(1)
The leaky LMS algorithm
142(3)
Linearly constrained LMS algorithm
145(5)
Self-correcting adaptive filtering (SCAF)
150(3)
Transform domain adaptive LMS filtering
153(5)
Error normalized LMS algorithms
158(13)
Problems
167(1)
Hints-solutions-suggestions
167(4)
Least-squares and recursive least-squares signal processing
171(32)
Introduction to least squares
171(1)
Least-square formulation
171(9)
Least-squares approach
180(2)
Orthogonality principle
182(2)
Projection operator
184(2)
Least-squares finite impulse response filter
186(2)
Introduction to RLS algorithm
188(15)
Problems
197(1)
Hints-solutions-suggestions
197(6)
Abbreviations
203(2)
Bibliography
205(2)
Appendix --- Matrix analysis
207(12)
Definitions
207(3)
Special matrices
210(2)
Matrix operation and formulas
212(3)
Eigen decomposition of matrices
215(2)
Matrix expectations
217(1)
Differentiation of a scalar function with respect to a vector
217(2)
Index 219


Alexander D. Poularikas, Zayed M. Ramadan