Adaptive Filter Theory

Simon Haykin

922 pages, parution le 25/10/2001 (4^eme édition)

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Résumé

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. This up-to-date book develops concepts in as unified and accessible a manner as possible. This highly successful book contains in-depth treatment 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, and the codes for which can be downloaded from the Web.

Covers a wide range of topics including Stochastic Processes, Wiener Filters, Kalman Filters, and many more.

For readers interested in learning about adaptive filters and the theories behind them.

L'auteur - Simon Haykin

is University Professor and Director of the Adaptive Systems Laboratory at McMaster University (Ontario, Canada)

Sommaire

1: Stochastic Processes
2: Wiener Filters
3: Linear Prediction
4: Method of Steepest Descent
5: Least-Mean-Square Adaptive Filters
6: Method of Least Squares
7: Recursive Least-Square Adaptive Filters
8: Kalman Filters
9: Square-Root Adaptive Filters
10:: Order-Recursive Adaptive Filters
11: Finite-Precision Effects
12: Tracking of Time-Varying Systems
13: Transform-Domain Adaptive Filters
14: Subspace Adaptive Filters
15: Nonlinear Adaptive Filters
Appendix A: Complex Variables
Appendix B: Differentiation with respect to a vector
Appendix C: Method of Lagrange Multipliers
Appendix D: Eigenanalysis
Appendix E: Rotations and Reflections
Appendix F: Complex Wishart Distribution
Bibliography

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Prentice Hall
Auteur(s)	Simon Haykin
Parution	25/10/2001
Édition	4^eme édition
Nb. de pages	922
Format	18 x 24
Couverture	Relié
Poids	1487g
Intérieur	Noir et Blanc
EAN13	9780130901262
ISBN13	978-0-13-090126-2