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01719cam a22002897a 4500 |
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armpuni |
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20160923153929.0 |
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160923s1996####xx#a##########000#0#und#d |
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|a armpuni
|c armpuni
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| 041 |
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|a en
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| 080 |
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|a 621.372
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| 100 |
1 |
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|a Haykin, Simon
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| 245 |
1 |
0 |
|a Adaptive filter theory /
|c Simon Haykin
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| 250 |
|
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|a third edition
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| 260 |
|
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|a New Jersey :
|b Prentice Hall,
|c 1996
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| 300 |
|
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|a xii, 989 p.:
|b il.;
|c 23 cm.
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| 500 |
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|a abbreviations p. 932
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| 500 |
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|a Appendix p.
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| 500 |
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|a Bibliography p. 941
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| 500 |
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|a Incluye glosario p. 928
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| 500 |
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|a Principal symbols p. 933
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| 550 |
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|a Linear filter structures. Backround material.Discrete-time signal processing. Stationary processes and models. Spectrum analysis. Eigenanalysis. Linear optimum filtering. Wiener filters. Linear prediction. Kalman filters. Linear adaptive filtering. Method of steepes descent. Least-Mean-Square algorithm. Frequency-domain adaptive filters. Method of least squares. Rotations and reflections. Recursive least-squares algorithm. Square-Root Adaptive filters. Order-recursive adaptive filters. Tracking of time-varyng systems. Finite-precision effects. Nonlinear adaptive filtering. Blind deconvolution. Back-propagation learning. Radial basis function networks. Complex variables. Differentiation with respect to a vector. Methods of lagrange multipliers. Estimation theory. Maximum-entropy method. Minimum-variance distortionless response spectrum. Grandient adaptive lattice algorithm. Steady-state analysis of the LMS Algorithm without invoking the independence assumption.
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| 650 |
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7 |
|a FILTROS
|2 LEMB
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| 942 |
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|c LB
|2 cdu
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| 945 |
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|a MDC
|d 1999-09-14
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| 999 |
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|c 4460
|d 5635
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