LEADER 05744nam 2200817 a 450 001 9910824343303321 005 20240314010700.0 010 $a9781118579558 010 $a1118579550 010 $a9781118579251 010 $a1118579259 010 $a9781118579664 010 $a1118579666 035 $a(CKB)2550000001105817 035 $a(EBL)1272227 035 $a(OCoLC)852758625 035 $a(SSID)ssj0001034892 035 $a(PQKBManifestationID)11599952 035 $a(PQKBTitleCode)TC0001034892 035 $a(PQKBWorkID)11028603 035 $a(PQKB)10134397 035 $a(OCoLC)853501537 035 $a(MiAaPQ)EBC1272227 035 $a(Au-PeEL)EBL1272227 035 $a(CaPaEBR)ebr10731719 035 $a(CaONFJC)MIL504487 035 $a(FINmELB)ELB178713 035 $a(Perlego)1001698 035 $a(EXLCZ)992550000001105817 100 $a20150303d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aProportionate-type normalized least mean square algorithms /$fKevin Wagner, Milos? Doroslovac?ki 205 $a1st ed. 210 $aLondon $cISTE ;$aHoboken, N.J. $cWiley$dc2013 215 $a1 online resource (184 p.) 225 0 $aDigital signal and image processing series 225 0 $aFocus Series 300 $aDescription based upon print version of record. 311 08$a9781848214705 311 08$a1848214707 311 08$a9781299732360 311 08$a1299732364 320 $aIncludes bibliographical references and index. 327 $aTitle 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 327 $aChapter 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 327 $a3.1. 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 327 $a4.1. 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 327 $a4.3. 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 327 $a5.2. Derivation of the Conditional PDF of WD for the PtLMS algorithm 330 $aThe topic of this book is proportionate-type normalized least mean squares (PtNLMS) adaptive filtering algorithms, which attempt to estimate an unknown impulse response by adaptively giving gains proportionate to an estimate of the impulse response and the current measured error. These algorithms offer low computational complexity and fast convergence times for sparse impulse responses in network and acoustic echo cancellation applications. New PtNLMS algorithms are developed by choosing gains that optimize user-defined criteria, such as mean square error, at all times. PtNLMS algorithms ar 410 0$aFocus series (London, England) 606 $aAlgorithms 606 $aComputer algorithms 606 $aEquations, Simultaneous$xNumerical solutions 615 0$aAlgorithms. 615 0$aComputer algorithms. 615 0$aEquations, Simultaneous$xNumerical solutions. 676 $a511.8 700 $aWagner$b Kevin$01711837 701 $aDoroslovac?ki$b Milos?$01711838 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910824343303321 996 $aProportionate-type normalized least mean square algorithms$94103454 997 $aUNINA