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| Авторизация |
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| Поиск по указателям |
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| Farhang-Boroujeny B. — Adaptive filters: theory and applications |
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| Предметный указатель |
Transform domain LMS algorithm, computational complexity 237
Transform domain LMS algorithm, effect or normalization 213
Transform domain LMS algorithm, filtering view 219—224
Transform domain LMS algorithm, geometrical interpretation of 211—215 217
Transform domain LMS algorithm, guidelines for the selection of the transform 224
Transform domain LMS algorithm, improvement factor 217
Transform domain LMS algorithm, improvement factor, maximum attainable improvement 219
Transform domain LMS algorithm, Karhunen — Loeve transform and 210
Transform domain LMS algorithm, misadjustment of 209
Transform domain LMS algorithm, modes of convergence 209
Transform domain LMS algorithm, Newton's algorithm and 210
Transform domain LMS algorithm, power-normalization and 208—209
Transform domain LMS algorithm, selection of transform 210—224
Transform domain LMS algorithm, step-normalization 208 225
Transform domain LMS algorithm, summary 209
Transform domain LMS algorithm, tracking behaviour 473 480 481 482 485
Transformation matrix 202
Transversal filter 4
Transversal filter based adaptive algorithms, fast block LMS algorithm see "Fast block LMS algorithm"
Transversal filter based adaptive algorithms, fast recursive least-squares algorithms see "Fast recursive least-squares algorithms"
Transversal filter based adaptive algorithms, subband adaptive fillers see "Subband adaptive fillers"
Transversal filter based adaptive algorithms, transform domain LMS algorithm see "Transform domain LMS algorithm"
Unconstrained Wiener filters 62—81
Unconstrained Wiener filters, inverse modelling 71—75
Unconstrained Wiener filters, modelling 68—70
Unconstrained Wiener filters, noise cancellation 75—81
Unconstrained Wiener filters, optimum transfer function of 66
Unconstrained Wiener filters, performance function of 63—64
Unconstrained Wiener filters, principle of orthogonality 66
Unconstrained Wiener filters, Wiener — Hopf equation 66
Unitary matrix 92 202
Unitary similarity transformation 93
Variable step-size LMS algorithm 175—178 485—489
Variable step-size LMS algorithm, bounds on the step-size parameters 177
Variable step-size LMS algorithm, computer simulations 489—494
Variable step-size LMS algorithm, derivation 466
Variable step-size LMS algorithm, optimal tracking behaviour 463 486 491
Variable step-size LMS algorithm, step-normalization 489
Variable step-size LMS algorithm, step-size parameters, limiting 491
Variable step-size LMS algorithm, step-size update recursions 176 487
Variable step-size LMS algorithm, summary 177
Variable step-size LMS algorithm, variations and extensions 487—489
Variable step-size LMS algorithm, variations and extensions, common step-size parameter, with a 488
Variable step-size LMS algorithm, variations and extensions, complex-valued data, for 488
| Variable step-size LMS algorithm, variations and extensions, multiplicative vs. linear increments 487 491
Variable step-size LMS algorithm, variations and extensions, sign update equation 487
Vector space of random variables 58
Viterbi detector 14
Volterra filters 6
Walsh — Hadamard transform (WHT) 225
Walsh — Hadamard transform (WHT), sliding realization of 244
Waveform coders 19—20
Weak excitation 131
Weight-error correlation matrix 147 149 426 474
Weight-error vector 142 426
Weighting functiori/factor 413 419
Wide-sense stationary processes see "Stochastic processes"
Wiener filters 2 49 see
Wiener filters, adaptive filters development based on theory of 7
Wiener filters, applications 49
Wiener filters, criterion 50
Wiener filters, example of performance function for I1R structure 65
Wiener filters, example of performance surface for FIR structure 56
Wiener filters, extension to complex-valued case 59—62
Wiener filters, minimum mean-square error 54 62
Wiener filters, non-recursive (FIR) 50
Wiener filters, optimum tap weights 54 62
Wiener filters, performance/cost function 50
Wiener filters, principle of correlation cancellation 70
Wiener filters, principle of orthogonality 56—58
Wiener filters, recursive (I1R) 50 324
Wiener filters, relationship with least-squares estimation 413
Wiener filters, summary 81—82
Wiener filters, transversal, real-valued case 51—56
Wiener — Hopf equation, complex-valued case 62
Wiener — Hopf equation, derivation using the principle or orthogonality 58
Wiener — Hopf equation, direct derivation 53
Wiener — Hopf equation, frequency domain interpretation of 67
Wiener — Hopf equation, real-valued case 54
Wiener — Hopf equation, solution using Levinson — Durbin algorithm 357 377—379
Wiener — Hopf equation, transform domain adaptive filters, for 203
Wiener — Hopf equation, unconstrained filters, for 66
Window matrices 256 287
z-transform 29—34
z-transform, examples 29—31
z-transform, inverse 31 33
z-transform, inverse integral 33
z-transform, region of convergence 29
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