Adaptive IIR Filtering in Signal Processing and ControlCRC Press, 25 okt 1994 - 704 pagina's Integrates rational approximation with adaptive filtering, providing viable, numerically reliable procedures for creating adaptive infinite impulse response (IIR) filters. The choice of filter structure to adapt, algorithm design and the approximation properties for each type of algorithm are also addressed. This work recasts the theory of adaptive IIR filters by concentrating on recursive lattice filters, freeing systems from the need for direct-form filters.;A solutions manual is available for instructors only. College or university bookstores may order five or more copies at a special student price which is available upon request. |
Inhoudsopgave
Introduction | 1 |
The BeurlingLax Theorem Hankel Forms | 82 |
Rational Approximation in Hankel Norm | 145 |
Rational H2 Approximation | 180 |
Stability of TimeVarying Recursive Filters | 228 |
Gradient Descent Algorithms | 258 |
The SteiglitzMcBride Family of Algorithms | 374 |
Overige edities - Alles bekijken
Veelvoorkomende woorden en zinsdelen
adaptation algorithm adaptive filter adaptive IIR filtering algorithm all-pass function anti-causal bandwidth Chapter computed Consider constraint cost function deduce deg Ĥ(z degree differential equation direct form filter Dk(z DM(z Do(z eigenvalues equation error error signal Example Figure filtered regressor Fk(z FM(z Fo(z function H(z gradient descent H₂ Hankel form Hankel singular values hyperstability IEEE Trans impulse response inner product L2 norm lattice algorithm lattice filter lattice form Lemma linear matrix minimize minimum phase minimum point Mth-order notch frequency obtained Ok+1 OM+1 optimal orthogonal orthonormal output error pole-zero cancellations poles polynomial positive definite prefilter properties rational function recursion reduced error surface result rotation angles Schur complement Section Signal Processing solution spectral density spectral density function stationary point Steiglitz-McBride iteration Su(ej Su(z subspace Suppose Szegö polynomials Theorem time-varying transfer function transformation undermodelled unit circle unknown system vector verify white noise yields zeros
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