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Projection methods for systems of equations [e-book] / Claude Brezinski
| Projection methods for systems of equations [e-book] / Claude Brezinski |
| Autore | Brezinski, Claude |
| Pubbl/distr/stampa | Amsterdam ; New York : Elsevier Science, 1997 |
| Descrizione fisica | vii, 400 p. : ill. ; 25 cm |
| Disciplina | 512.942 |
| Collana | Studies in computational mathematics ; 7 |
| Soggetto topico |
Equations, Simultaneous - Numerical solutions
Iterative methods (Mathematics) |
| ISBN |
9780444827777
0444827773 |
| Formato | Risorse elettroniche  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991003279279707536 |
Brezinski, Claude
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| Amsterdam ; New York : Elsevier Science, 1997 | ||
| Risorse elettroniche | ||
| Lo trovi qui: Univ. del Salento | ||
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Proportionate-type normalized least mean square algorithms [[electronic resource] /] / Kevin Wagner, Miloš Doroslovački
| Proportionate-type normalized least mean square algorithms [[electronic resource] /] / Kevin Wagner, Miloš Doroslovački |
| Autore | Wagner Kevin |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (184 p.) |
| Disciplina | 511.8 |
| Altri autori (Persone) | DoroslovačkiMiloš |
| Collana |
Digital signal and image processing series
Focus Series |
| Soggetto topico |
Algorithms
Computer algorithms Equations, Simultaneous - Numerical solutions |
| ISBN |
1-118-57955-0
1-118-57925-9 1-118-57966-6 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Title Page; Contents; Preface; Notation; Acronyms; Chapter 1. Introduction to PtNLMS Algorithms; 1.1. Applications motivating PtNLMS algorithms; 1.2. Historical review of existing PtNLMS algorithms; 1.3. Unified framework for representing PtNLMS algorithms; 1.4. Proportionate-type NLMS adaptive filtering algorithms; 1.4.1. Proportionate-type least mean square algorithm; 1.4.2. PNLMS algorithm; 1.4.3. PNLMS++ algorithm; 1.4.4. IPNLMS algorithm; 1.4.5. IIPNLMS algorithm; 1.4.6. IAF-PNLMS algorithm; 1.4.7. MPNLMS algorithm; 1.4.8. EPNLMS algorithm; 1.5. Summary
Chapter 2. LMS Analysis Techniques2.1. LMS analysis based on small adaptation step-size; 2.1.1. Statistical LMS theory: small step-size assumptions; 2.1.2. LMS analysis using stochastic difference equations with constant coefficients; 2.2. LMS analysis based on independent input signal assumptions; 2.2.1. Statistical LMS theory: independent input signal assumptions; 2.2.2. LMS analysis using stochastic difference equations with stochastic coefficients; 2.3. Performance of statistical LMS theory; 2.4. Summary; 3. PtNLMS Analysis Techniques 3.1. Transient analysis of PtNLMS algorithm for white input3.1.1. Link between MSWD and MSE; 3.1.2. Recursive calculation of the MWD and MSWD for PtNLMS algorithms; 3.2. Steady-state analysis of PtNLMS algorithm: bias and MSWD calculation; 3.3. Convergence analysis of the simplified PNLMS algorithm; 3.3.1. Transient theory and results; 3.3.2. Steady-state theory and results; 3.4. Convergence analysis of the PNLMS algorithm; 3.4.1. Transient theory and results; 3.4.2. Steady-state theory and results; 3.5. Summary; 4. Algorithms Designed Based on Minimization of User-Defined Criteria 4.1. PtNLMS algorithms with gain allocation motivated by MSE minimization for white input4.1.1. Optimal gain calculation resulting from MMSE; 4.1.2. Water-filling algorithm simplifications; 4.1.3. Implementation of algorithms; 4.1.4. Simulation results; 4.2. PtNLMS algorithm obtained by minimization of MSE modeled by exponential functions; 4.2.1. WD for proportionate-type steepest descent algorithm; 4.2.2. Water-filling gain allocation for minimization of the MSE modeled by exponential functions; 4.2.3. Simulation results 4.3. PtNLMS algorithm obtained by minimization of the MSWD for colored input4.3.1. Optimal gain algorithm; 4.3.2. Relationship between minimization of MSE and MSWD; 4.3.3. Simulation results; 4.4. Reduced computational complexity suboptimal gain allocation for PtNLMS algorithm with colored input; 4.4.1. Suboptimal gain allocation algorithms; 4.4.2. Simulation results; 4.5. Summary; Chapter 5. Probability Density of WD for PtLMS Algorithms; 5.1. Proportionate-type least mean square algorithms; 5.1.1. Weight deviation recursion 5.2. Derivation of the Conditional PDF of WD for the PtLMS algorithm |
| Record Nr. | UNINA-9910139041203321 |
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Wagner Kevin
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| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Proportionate-type normalized least mean square algorithms / / Kevin Wagner, Miloš Doroslovački
| Proportionate-type normalized least mean square algorithms / / Kevin Wagner, Miloš Doroslovački |
| Autore | Wagner Kevin |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | London, : ISTE |
| Descrizione fisica | 1 online resource (184 p.) |
| Disciplina | 511.8 |
| Altri autori (Persone) | DoroslovačkiMiloš |
| Collana |
Digital signal and image processing series
Focus Series |
| Soggetto topico |
Algorithms
Computer algorithms Equations, Simultaneous - Numerical solutions |
| ISBN |
9781118579558
1118579550 9781118579251 1118579259 9781118579664 1118579666 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Title Page; Contents; Preface; Notation; Acronyms; Chapter 1. Introduction to PtNLMS Algorithms; 1.1. Applications motivating PtNLMS algorithms; 1.2. Historical review of existing PtNLMS algorithms; 1.3. Unified framework for representing PtNLMS algorithms; 1.4. Proportionate-type NLMS adaptive filtering algorithms; 1.4.1. Proportionate-type least mean square algorithm; 1.4.2. PNLMS algorithm; 1.4.3. PNLMS++ algorithm; 1.4.4. IPNLMS algorithm; 1.4.5. IIPNLMS algorithm; 1.4.6. IAF-PNLMS algorithm; 1.4.7. MPNLMS algorithm; 1.4.8. EPNLMS algorithm; 1.5. Summary
Chapter 2. LMS Analysis Techniques2.1. LMS analysis based on small adaptation step-size; 2.1.1. Statistical LMS theory: small step-size assumptions; 2.1.2. LMS analysis using stochastic difference equations with constant coefficients; 2.2. LMS analysis based on independent input signal assumptions; 2.2.1. Statistical LMS theory: independent input signal assumptions; 2.2.2. LMS analysis using stochastic difference equations with stochastic coefficients; 2.3. Performance of statistical LMS theory; 2.4. Summary; 3. PtNLMS Analysis Techniques 3.1. Transient analysis of PtNLMS algorithm for white input3.1.1. Link between MSWD and MSE; 3.1.2. Recursive calculation of the MWD and MSWD for PtNLMS algorithms; 3.2. Steady-state analysis of PtNLMS algorithm: bias and MSWD calculation; 3.3. Convergence analysis of the simplified PNLMS algorithm; 3.3.1. Transient theory and results; 3.3.2. Steady-state theory and results; 3.4. Convergence analysis of the PNLMS algorithm; 3.4.1. Transient theory and results; 3.4.2. Steady-state theory and results; 3.5. Summary; 4. Algorithms Designed Based on Minimization of User-Defined Criteria 4.1. PtNLMS algorithms with gain allocation motivated by MSE minimization for white input4.1.1. Optimal gain calculation resulting from MMSE; 4.1.2. Water-filling algorithm simplifications; 4.1.3. Implementation of algorithms; 4.1.4. Simulation results; 4.2. PtNLMS algorithm obtained by minimization of MSE modeled by exponential functions; 4.2.1. WD for proportionate-type steepest descent algorithm; 4.2.2. Water-filling gain allocation for minimization of the MSE modeled by exponential functions; 4.2.3. Simulation results 4.3. PtNLMS algorithm obtained by minimization of the MSWD for colored input4.3.1. Optimal gain algorithm; 4.3.2. Relationship between minimization of MSE and MSWD; 4.3.3. Simulation results; 4.4. Reduced computational complexity suboptimal gain allocation for PtNLMS algorithm with colored input; 4.4.1. Suboptimal gain allocation algorithms; 4.4.2. Simulation results; 4.5. Summary; Chapter 5. Probability Density of WD for PtLMS Algorithms; 5.1. Proportionate-type least mean square algorithms; 5.1.1. Weight deviation recursion 5.2. Derivation of the Conditional PDF of WD for the PtLMS algorithm |
| Record Nr. | UNINA-9910824343303321 |
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Wagner Kevin
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| London, : ISTE | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Rank-deficient and discrete ill-posed problems : numerical aspects of linear inversion / Per Christian Hansen
| Rank-deficient and discrete ill-posed problems : numerical aspects of linear inversion / Per Christian Hansen |
| Autore | Hansen, Per Christian |
| Pubbl/distr/stampa | Philadelphia : SIAM, c1998 |
| Descrizione fisica | xvi, 247 p. : ill. ; 26 cm |
| Disciplina | 512.942 |
| Collana | SIAM monographs on mathematical modeling and computation |
| Soggetto topico |
Equations, Simultaneous - Numerical solutions
Iterative methods (Mathematics) Sparse matrices |
| ISBN | 0898714036 |
| Classificazione |
AMS 65F05
LC QA218.H38 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991002520099707536 |
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Hansen, Per Christian
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| Philadelphia : SIAM, c1998 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
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