Design of momentum LMS adaptive strategy for parameter estimation of Hammerstein controlled autoregressive systems
In the present work, strength of momentum least mean square (MLMS) algorithm is exploited for nonlinear system identification problems represented with Hammerstein model. The MLMS algorithm uses the previous gradient information to estimate the current weights instead of using only current value of...
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| Published in | Neural computing & applications Vol. 30; no. 4; pp. 1133 - 1143 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.08.2018
Springer Nature B.V |
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| Online Access | Get full text |
| ISSN | 0941-0643 1433-3058 |
| DOI | 10.1007/s00521-016-2762-1 |
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| Abstract | In the present work, strength of momentum least mean square (MLMS) algorithm is exploited for nonlinear system identification problems represented with Hammerstein model. The MLMS algorithm uses the previous gradient information to estimate the current weights instead of using only current value of gradient; thus, it is faster in the convergence and less probable to trap in local minima. The perfection of the design scheme is certified through effective parameter estimation of nonlinear Hammerstein control autoregressive models. The robustness of the scheme is established by examining the performance for different levels of noise variance. The performance comparison through mean square error and Nash–Sutcliffe efficiency parameters, calculated for sufficiently large number of multiple runs, proves the intrinsic worth of MLMS algorithm in system identification. |
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| AbstractList | In the present work, strength of momentum least mean square (MLMS) algorithm is exploited for nonlinear system identification problems represented with Hammerstein model. The MLMS algorithm uses the previous gradient information to estimate the current weights instead of using only current value of gradient; thus, it is faster in the convergence and less probable to trap in local minima. The perfection of the design scheme is certified through effective parameter estimation of nonlinear Hammerstein control autoregressive models. The robustness of the scheme is established by examining the performance for different levels of noise variance. The performance comparison through mean square error and Nash–Sutcliffe efficiency parameters, calculated for sufficiently large number of multiple runs, proves the intrinsic worth of MLMS algorithm in system identification. |
| Author | Chaudhary, Naveed Ishtiaq Raja, Muhammad Asif Zahoor Zubair, Syed |
| Author_xml | – sequence: 1 givenname: Naveed Ishtiaq surname: Chaudhary fullname: Chaudhary, Naveed Ishtiaq email: naveedch835@gmail.com, naveed.ishtiaq@iiu.edu.pk organization: Department of Electronic Engineering, International Islamic University – sequence: 2 givenname: Syed surname: Zubair fullname: Zubair, Syed organization: Department of Electronic Engineering, International Islamic University – sequence: 3 givenname: Muhammad Asif Zahoor surname: Raja fullname: Raja, Muhammad Asif Zahoor organization: Department of Electrical Engineering, COMSATS Institute of Information Technology |
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| CitedBy_id | crossref_primary_10_1007_s00521_018_3362_z crossref_primary_10_1016_j_apm_2020_12_035 crossref_primary_10_1142_S0217979223502545 crossref_primary_10_1016_j_asoc_2019_03_052 crossref_primary_10_1002_acs_3471 crossref_primary_10_1115_1_4045891 crossref_primary_10_1016_j_dsp_2024_104483 crossref_primary_10_1002_dac_4915 crossref_primary_10_1140_epjp_s13360_023_04626_6 crossref_primary_10_1016_j_apm_2018_09_028 crossref_primary_10_1140_epjp_i2019_12785_8 crossref_primary_10_1038_s41598_024_83654_3 crossref_primary_10_1007_s00521_019_04328_0 crossref_primary_10_1016_j_apm_2020_03_014 crossref_primary_10_32604_cmc_2022_019120 crossref_primary_10_1016_j_jfranklin_2018_12_002 crossref_primary_10_1155_2022_4851364 |
| Cites_doi | 10.1007/s12555-012-0322-8 10.1016/j.jfranklin.2014.09.025 10.1109/TAC.2013.2273283 10.1007/s11071-015-2548-5 10.1016/j.sigpro.2014.11.016 10.1007/s11071-016-3058-9 10.1049/iet-spr.2013.0438 10.1016/j.apm.2012.04.039 10.1016/j.amc.2014.09.070 10.1007/s13369-014-1289-y 10.1007/s13369-015-1698-6 10.1002/9781118535561 10.1007/s00034-015-0190-6 10.1016/j.sigpro.2015.02.010 10.1016/j.sigpro.2014.06.015 10.1016/j.ins.2016.03.037 10.1007/s00034-014-9772-y 10.1007/s00034-014-9839-9 10.1016/j.cam.2015.03.057 10.1007/s00521-014-1716-8 10.3182/20140824-6-ZA-1003.01754 10.1007/s11071-014-1771-9 10.1155/2013/467276 10.1007/s00521-016-2677-x 10.1016/j.ifacol.2015.12.159 10.1016/j.sigpro.2015.10.009 10.1109/TSP.2013.2286103 10.1049/iet-cta.2015.1195 10.1007/s00521-016-2394-5 10.1109/TSP.2007.907805 10.1016/j.jsv.2010.09.012 10.1007/s11071-014-1791-5 10.1007/s11071-013-1168-1 10.1109/TCST.2014.2387216 10.1016/j.ins.2012.07.064 10.1016/j.ymssp.2011.06.010 10.1016/j.ces.2015.02.021 10.1049/iet-cta.2012.0313 10.1007/s00521-010-0461-x 10.1007/s11071-014-1804-4 10.1007/s00521-016-2548-5 10.1007/s11071-014-1748-8 10.1007/978-0-85729-522-4 10.1016/j.automatica.2016.05.024 10.1016/j.aml.2012.03.038 10.1007/s11071-015-2279-7 10.1016/j.amc.2014.02.087 10.1016/j.neucom.2015.04.022 10.1016/j.sigpro.2015.04.015 10.1007/978-1-84996-513-2_4 10.1007/s00034-016-0378-4 10.1016/j.aml.2015.12.018 10.1016/j.aml.2016.03.016 10.1007/s11071-014-1801-7 10.1016/j.measurement.2013.11.010 10.1186/s13634-015-0219-9 10.1016/j.dsp.2015.07.002 |
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| Keywords | Signal processing Momentum LMS System identification Hammerstein models Adaptive algorithms |
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| References | BillingsSANonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains2013ChichesterWiley10.1002/97811185355611287.93101 ChaudharyNIRajaMAZKhanAURDesign of modified fractional adaptive strategies for Hammerstein nonlinear control autoregressive systemsNonlinear Dyn20158241811183010.1007/s11071-015-2279-7 KeesmanKJSystem identification: an introduction2011BerlinSpringer10.1007/978-0-85729-522-41230.93001 MoinuddinMAl-SaggafUMAhmedAFamily of state space least mean power of two-based algorithmsEURASIP J Adv Signal Process201520151110.1186/s13634-015-0219-9 VörösJIterative identification of nonlinear dynamic systems with output backlash using three-block cascade modelsNonlinear Dyn201579321872195330542410.1007/s11071-014-1804-4 XuLChenLXiongWParameter estimation and controller design for dynamic systems from the step responses based on the Newton iterationNonlinear Dyn201579321552163330542110.1007/s11071-014-1801-7 ChenHDingFXiaoYDecomposition-based least squares parameter estimation algorithm for input nonlinear systems using the key term separation techniqueNonlinear Dyn20157932027203510.1007/s11071-014-1791-51331.93083 HuHDingRLeast squares based iterative identification algorithms for input nonlinear controlled autoregressive systems based on the auxiliary modelNonlinear Dyn2014761777784318920910.1007/s11071-013-1168-11319.93081 AlexanderTSAdaptive signal processing: theory and applications2012BerlinSpringer ElleuchKKharratMChaariAChaabaneMModeling and identification of block-oriented heat transfer processInt J Inf Syst Sci20095141561158.93408 BoZSunCXuYJiangSA variable momentum factor filtered-x weighted accumulated LMS algorithm for narrowband active noise control systemsMeasurement20144828229110.1016/j.measurement.2013.11.010 WangDHierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed modelsAppl Math Lett2016571319346410110.1016/j.aml.2015.12.0181336.93155 CorbierCEl BadaouiMUgaldeHMRHuberian approach for reduced order ARMA modeling of neurodegenerative disorder signalSignal Process201511327328410.1016/j.sigpro.2015.02.010 ChaudharyNIRajaMAZAslamMSAhmedNNovel generalization of Volterra LMS algorithm to fractional order with application to system identificationNeural Comput Appl2016 DingFDengKLiuXDecomposition based Newton iterative identification method for a Hammerstein nonlinear FIR system with ARMA noiseCircuits Syst Signal Process201433928812893324590910.1007/s00034-014-9772-y ZhaoWZhengWXBaiEWA recursive local linear estimator for identification of nonlinear ARX systems: asymptotical convergence and applicationsIEEE Trans Automat Control2013581230543069315226810.1109/TAC.2013.22732831369.93611 YuCZhangCXieLA new deterministic identification approach to Hammerstein systemsIEEE Trans Signal Process2014621131140314959910.1109/TSP.2013.2286103 WangDDingFParameter estimation algorithms for multivariable Hammerstein CARMA systemsInf Sci2016355237248351529310.1016/j.ins.2016.03.037 RajaMAZChaudharyNITwo-stage fractional least mean square identification algorithm for parameter estimation of CARMA systemsSignal Process201510732733910.1016/j.sigpro.2014.06.015 XuLThe damping iterative parameter identification method for dynamical systems based on the sine signal measurementSignal Process201612066066710.1016/j.sigpro.2015.10.009 TaoualiOElaissiIMessaoudHOnline identification of nonlinear system using reduced kernel principal component analysisNeural Comput Appl201221116116910.1007/s00521-010-0461-x Vörös J (2010) Compound operator decomposition and its application to Hammerstein and Wiener systems. In: Giri F, Bai E-W (eds) Block-oriented nonlinear system identification. Springer, London, pp 35–51. doi:10.1007/978-1-84996-513-2_4 Chaudhary NI, Raja MAZ, Khan JA, Aslam MS (2013) Identification of input nonlinear control autoregressive systems using fractional signal processing approach. Sci World J, Article ID 467276. doi:10.1155/2013/467276 MaoYDingFMulti-innovation stochastic gradient identification for Hammerstein controlled autoregressive systems based on the filtering techniqueNonlinear Dyn20157931745175510.1007/s11071-014-1771-91331.93211 TogunNBaysecSKaraTNonlinear modeling and identification of a spark ignition engine torqueMech Syst Signal Process20122629430410.1016/j.ymssp.2011.06.010 NatkeHGApplication of system identification in engineering2014BerlinSpringer0651.93001 XuLDingFRecursive least squares and multi-innovation stochastic gradient parameter estimation methods for signal modelingCircuits Syst Signal Process20171370.94286 ZhangYLiNChambersJASayedAHSteady-state performance analysis of a variable tap-length LMS algorithmIEEE Trans Signal Process2008562839845251244710.1109/TSP.2007.90780506896420 UgaldeHMRCarmonaJCReyes-ReyesJAlvaradoVMMantillaJComputational cost improvement of neural network models in black box nonlinear system identificationNeurocomputing20151669610810.1016/j.neucom.2015.04.022 XuLApplication of the Newton iteration algorithm to the parameter estimation for dynamical systemsJ Comput Appl Math20152883343334960210.1016/j.cam.2015.03.0571314.93062 RajaMAZShahAAMehmoodAChaudharyNIAslamMSBio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive systemNeural Comput Appl2016 Al-SaggafUMMoinuddinMArifMZerguineAThe q-least mean squares algorithmSignal Process2015111506010.1016/j.sigpro.2014.11.016 law PawlakMLvJNonparametric specification testing for Hammerstein systemsIFAC Pap Online2015482839239710.1016/j.ifacol.2015.12.159 DingFWangXChenQXiaoYRecursive least squares parameter estimation for a class of output nonlinear systems based on the model decompositionCircuits Syst Signal Process201635933233338352975810.1007/s00034-015-0190-61345.93169 WangDQLiuHBDingFHighly efficient identification methods for dual-rate Hammerstein systemsIEEE Trans Control Syst Technol20152351952196010.1109/TCST.2014.2387216 XiaoYSongGLiaoYDingRMulti-innovation stochastic gradient parameter estimation for input nonlinear controlled autoregressive modelsInt J Control Automat Syst201210363964310.1007/s12555-012-0322-8 WangYDingFRecursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the data filteringNonlinear Dyn201684210451053347494610.1007/s11071-015-2548-51354.93158 UgaldeHMRCarmonaJCReyes-ReyesJAlvaradoVMCorbierCBalanced simplicity–accuracy neural network model families for system identificationNeural Comput Appl201526117118610.1007/s00521-014-1716-8 LiJParameter estimation for Hammerstein CARARMA systems based on the Newton iterationAppl Math Lett20132619196297140610.1016/j.aml.2012.03.0381255.65119 DingFLiuXGChuJGradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principleIET Control Theory Appl201372176184308745510.1049/iet-cta.2012.0313 XuLA proportional differential control method for a time-delay system using the Taylor expansion approximationAppl Math Comput201423639139931977361334.93125 ChenHXiaoYDingFHierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principleAppl Math Comput20142471202121032709181343.62055 KhaniFHaeriMRobust model predictive control of nonlinear processes represented by Wiener or Hammerstein modelsChem Eng Sci201512922323110.1016/j.ces.2015.02.021 RajaMAZChaudharyNIAdaptive strategies for parameter estimation of Box–Jenkins systemsIET Signal Process20148996898010.1049/iet-spr.2013.0438 WangDDingFChuYData filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principleInf Sci2013222203212299850910.1016/j.ins.2012.07.0641293.93758 WangYDingFFiltering-based iterative identification for multivariable systemsIET Control Theory Appl2016108894902352537210.1049/iet-cta.2015.1195 ChaudharyNIRajaMAZDesign of fractional adaptive strategy for input nonlinear Box–Jenkins systemsSignal Process201511614115110.1016/j.sigpro.2015.04.015 ChenHDingFHierarchical least squares identification for Hammerstein nonlinear controlled autoregressive systemsCircuits Syst Signal Process20153416175329915910.1007/s00034-014-9839-91341.93089 DingFHierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modelingAppl Math Model201337416941704300227210.1016/j.apm.2012.04.0391349.93391 AslamMSChaudharyNIRajaMAZA sliding-window approximation-based fractional adaptive strategy for Hammerstein nonlinear ARMAX systemsNonlinear Dyn20161371.93205 MaoYDingFA novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systemsDigit Signal Proc201546215225341171410.1016/j.dsp.2015.07.002 VörösJIdentification of nonlinear dynamic systems with input saturation and output backlash using three-block cascade modelsJ Franklin Inst20143511254555466327665010.1016/j.jfranklin.2014.09.025 Tang Y, Han Z, Wang Y, Zhang L, Lian Q (2016) A changing forgetting factor RLS for online identification of nonlinear systems based on ELM–Hammerstein model. Neural Comput Appl 1–15. doi:10.1007/s00521-016-2394-5 WangYDingFNovel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary modelAutomatica201671308313352198210.1016/j.automatica.2016.05.0241343.93087 MoinuddinMZerguineAA unified performance analysis of the family of normalized least mean algorithmsArab J Sci Eng2014391071457157325519910.1007/s13369-014-1289-y06882893 RébillatMHennequinRCorteelEKatzBFIdentification of cascade of Hammerstein models for the description of nonlinearities in vibrating devicesJ Sound Vib201133051018103810.1016/j.jsv.2010.09.012 ChaudharyNIRajaMAZIdentification of Hammerstein nonlinear ARMAX systems using nonlinear adaptive algorithmsNonlinear Dyn201579213851397330277510.1007/s11071-014-1748-81345.93045 HolcombCMde CallafonRABitmeadRRClosed-loop identification of Hammerstein systems with application to gas turbinesIFAC Proc201447349349810.3182/ F Ding (2762_CR34) 2013; 7 UM Al-Saggaf (2762_CR58) 2015; 111 MAZ Raja (2762_CR6) 2015; 107 M Rébillat (2762_CR26) 2011; 330 NI Chaudhary (2762_CR53) 2015; 82 L Xu (2762_CR11) 2015; 79 DQ Wang (2762_CR36) 2015; 23 M Moinuddin (2762_CR56) 2015; 2015 H Chen (2762_CR50) 2015; 79 CM Holcomb (2762_CR23) 2014; 47 L Xu (2762_CR8) 2014; 236 F Ding (2762_CR9) 2016; 35 NI Chaudhary (2762_CR44) 2015; 79 C Yu (2762_CR31) 2014; 62 Y Xiao (2762_CR47) 2012; 10 W Zhao (2762_CR28) 2013; 58 L Xu (2762_CR10) 2015; 288 F Ding (2762_CR41) 2014; 33 Y Wang (2762_CR15) 2016; 71 Y Mao (2762_CR40) 2015; 46 J Li (2762_CR32) 2013; 26 Y Zhang (2762_CR55) 2008; 56 SA Billings (2762_CR27) 2013 H Chen (2762_CR38) 2014; 247 N Togun (2762_CR24) 2012; 26 L Xu (2762_CR12) 2016; 120 K Elleuch (2762_CR25) 2009; 5 C Corbier (2762_CR2) 2015; 113 D Wang (2762_CR37) 2013; 222 D Wang (2762_CR33) 2016; 57 2762_CR30 M Moinuddin (2762_CR57) 2014; 39 J Vörös (2762_CR20) 2014; 351 O Taouali (2762_CR5) 2012; 21 HMR Ugalde (2762_CR7) 2015; 26 MAZ Raja (2762_CR3) 2014; 8 F Ding (2762_CR35) 2013; 37 M law Pawlak (2762_CR29) 2015; 48 MS Aslam (2762_CR46) 2016 MAZ Raja (2762_CR51) 2016 Y Mao (2762_CR43) 2016; 60 (2762_CR1) 2014 2762_CR21 TS Alexander (2762_CR54) 2012 A Ahmed (2762_CR59) 2016; 41 NI Chaudhary (2762_CR45) 2015; 116 NI Chaudhary (2762_CR18) 2016 Y Wang (2762_CR14) 2016; 84 F Khani (2762_CR22) 2015; 129 H Chen (2762_CR49) 2015; 34 H Hu (2762_CR48) 2014; 76 J Vörös (2762_CR19) 2015; 79 KJ Keesman (2762_CR17) 2011 Z Bo (2762_CR60) 2014; 48 2762_CR52 HMR Ugalde (2762_CR4) 2015; 166 Y Wang (2762_CR16) 2016; 10 L Xu (2762_CR13) 2017 D Wang (2762_CR42) 2016; 355 Y Mao (2762_CR39) 2015; 79 |
| References_xml | – reference: Tang Y, Han Z, Wang Y, Zhang L, Lian Q (2016) A changing forgetting factor RLS for online identification of nonlinear systems based on ELM–Hammerstein model. Neural Comput Appl 1–15. doi:10.1007/s00521-016-2394-5 – reference: ChaudharyNIRajaMAZDesign of fractional adaptive strategy for input nonlinear Box–Jenkins systemsSignal Process201511614115110.1016/j.sigpro.2015.04.015 – reference: ChenHDingFHierarchical least squares identification for Hammerstein nonlinear controlled autoregressive systemsCircuits Syst Signal Process20153416175329915910.1007/s00034-014-9839-91341.93089 – reference: AlexanderTSAdaptive signal processing: theory and applications2012BerlinSpringer – reference: XuLThe damping iterative parameter identification method for dynamical systems based on the sine signal measurementSignal Process201612066066710.1016/j.sigpro.2015.10.009 – reference: ZhangYLiNChambersJASayedAHSteady-state performance analysis of a variable tap-length LMS algorithmIEEE Trans Signal Process2008562839845251244710.1109/TSP.2007.90780506896420 – reference: AslamMSChaudharyNIRajaMAZA sliding-window approximation-based fractional adaptive strategy for Hammerstein nonlinear ARMAX systemsNonlinear Dyn20161371.93205 – reference: DingFLiuXGChuJGradient-based and least-squares-based iterative algorithms for Hammerstein systems using the hierarchical identification principleIET Control Theory Appl201372176184308745510.1049/iet-cta.2012.0313 – reference: WangDQLiuHBDingFHighly efficient identification methods for dual-rate Hammerstein systemsIEEE Trans Control Syst Technol20152351952196010.1109/TCST.2014.2387216 – reference: AhmedAMoinuddinMAl-SaggafUMState Space least mean fourth algorithm for dynamic state estimation in power systemsArab J Sci Eng201641252754310.1007/s13369-015-1698-6 – reference: ChaudharyNIRajaMAZKhanAURDesign of modified fractional adaptive strategies for Hammerstein nonlinear control autoregressive systemsNonlinear Dyn20158241811183010.1007/s11071-015-2279-7 – reference: RébillatMHennequinRCorteelEKatzBFIdentification of cascade of Hammerstein models for the description of nonlinearities in vibrating devicesJ Sound Vib201133051018103810.1016/j.jsv.2010.09.012 – reference: XuLDingFRecursive least squares and multi-innovation stochastic gradient parameter estimation methods for signal modelingCircuits Syst Signal Process20171370.94286 – reference: WangYDingFNovel data filtering based parameter identification for multiple-input multiple-output systems using the auxiliary modelAutomatica201671308313352198210.1016/j.automatica.2016.05.0241343.93087 – reference: WangDHierarchical parameter estimation for a class of MIMO Hammerstein systems based on the reframed modelsAppl Math Lett2016571319346410110.1016/j.aml.2015.12.0181336.93155 – reference: Vörös J (2010) Compound operator decomposition and its application to Hammerstein and Wiener systems. In: Giri F, Bai E-W (eds) Block-oriented nonlinear system identification. Springer, London, pp 35–51. doi:10.1007/978-1-84996-513-2_4 – reference: NatkeHGApplication of system identification in engineering2014BerlinSpringer0651.93001 – reference: HuHDingRLeast squares based iterative identification algorithms for input nonlinear controlled autoregressive systems based on the auxiliary modelNonlinear Dyn2014761777784318920910.1007/s11071-013-1168-11319.93081 – reference: RajaMAZShahAAMehmoodAChaudharyNIAslamMSBio-inspired computational heuristics for parameter estimation of nonlinear Hammerstein controlled autoregressive systemNeural Comput Appl2016 – reference: HolcombCMde CallafonRABitmeadRRClosed-loop identification of Hammerstein systems with application to gas turbinesIFAC Proc201447349349810.3182/20140824-6-ZA-1003.01754 – reference: UgaldeHMRCarmonaJCReyes-ReyesJAlvaradoVMCorbierCBalanced simplicity–accuracy neural network model families for system identificationNeural Comput Appl201526117118610.1007/s00521-014-1716-8 – reference: WangDDingFParameter estimation algorithms for multivariable Hammerstein CARMA systemsInf Sci2016355237248351529310.1016/j.ins.2016.03.037 – reference: MoinuddinMZerguineAA unified performance analysis of the family of normalized least mean algorithmsArab J Sci Eng2014391071457157325519910.1007/s13369-014-1289-y06882893 – reference: DingFDengKLiuXDecomposition based Newton iterative identification method for a Hammerstein nonlinear FIR system with ARMA noiseCircuits Syst Signal Process201433928812893324590910.1007/s00034-014-9772-y – reference: WangYDingFFiltering-based iterative identification for multivariable systemsIET Control Theory Appl2016108894902352537210.1049/iet-cta.2015.1195 – reference: DingFWangXChenQXiaoYRecursive least squares parameter estimation for a class of output nonlinear systems based on the model decompositionCircuits Syst Signal Process201635933233338352975810.1007/s00034-015-0190-61345.93169 – reference: KeesmanKJSystem identification: an introduction2011BerlinSpringer10.1007/978-0-85729-522-41230.93001 – reference: Chaudhary NI, Raja MAZ, Khan JA, Aslam MS (2013) Identification of input nonlinear control autoregressive systems using fractional signal processing approach. Sci World J, Article ID 467276. doi:10.1155/2013/467276 – reference: KhaniFHaeriMRobust model predictive control of nonlinear processes represented by Wiener or Hammerstein modelsChem Eng Sci201512922323110.1016/j.ces.2015.02.021 – reference: WangDDingFChuYData filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principleInf Sci2013222203212299850910.1016/j.ins.2012.07.0641293.93758 – reference: CorbierCEl BadaouiMUgaldeHMRHuberian approach for reduced order ARMA modeling of neurodegenerative disorder signalSignal Process201511327328410.1016/j.sigpro.2015.02.010 – reference: ChaudharyNIRajaMAZAslamMSAhmedNNovel generalization of Volterra LMS algorithm to fractional order with application to system identificationNeural Comput Appl2016 – reference: XuLA proportional differential control method for a time-delay system using the Taylor expansion approximationAppl Math Comput201423639139931977361334.93125 – reference: TogunNBaysecSKaraTNonlinear modeling and identification of a spark ignition engine torqueMech Syst Signal Process20122629430410.1016/j.ymssp.2011.06.010 – reference: RajaMAZChaudharyNITwo-stage fractional least mean square identification algorithm for parameter estimation of CARMA systemsSignal Process201510732733910.1016/j.sigpro.2014.06.015 – reference: MaoYDingFA novel parameter separation based identification algorithm for Hammerstein systemsAppl Math Lett2016602127350584810.1016/j.aml.2016.03.0161339.93109 – reference: BoZSunCXuYJiangSA variable momentum factor filtered-x weighted accumulated LMS algorithm for narrowband active noise control systemsMeasurement20144828229110.1016/j.measurement.2013.11.010 – reference: TaoualiOElaissiIMessaoudHOnline identification of nonlinear system using reduced kernel principal component analysisNeural Comput Appl201221116116910.1007/s00521-010-0461-x – reference: ChaudharyNIRajaMAZIdentification of Hammerstein nonlinear ARMAX systems using nonlinear adaptive algorithmsNonlinear Dyn201579213851397330277510.1007/s11071-014-1748-81345.93045 – reference: VörösJIterative identification of nonlinear dynamic systems with output backlash using three-block cascade modelsNonlinear Dyn201579321872195330542410.1007/s11071-014-1804-4 – reference: YuCZhangCXieLA new deterministic identification approach to Hammerstein systemsIEEE Trans Signal Process2014621131140314959910.1109/TSP.2013.2286103 – reference: law PawlakMLvJNonparametric specification testing for Hammerstein systemsIFAC Pap Online2015482839239710.1016/j.ifacol.2015.12.159 – reference: ZhaoWZhengWXBaiEWA recursive local linear estimator for identification of nonlinear ARX systems: asymptotical convergence and applicationsIEEE Trans Automat Control2013581230543069315226810.1109/TAC.2013.22732831369.93611 – reference: DingFHierarchical multi-innovation stochastic gradient algorithm for Hammerstein nonlinear system modelingAppl Math Model201337416941704300227210.1016/j.apm.2012.04.0391349.93391 – reference: XiaoYSongGLiaoYDingRMulti-innovation stochastic gradient parameter estimation for input nonlinear controlled autoregressive modelsInt J Control Automat Syst201210363964310.1007/s12555-012-0322-8 – reference: MaoYDingFMulti-innovation stochastic gradient identification for Hammerstein controlled autoregressive systems based on the filtering techniqueNonlinear Dyn20157931745175510.1007/s11071-014-1771-91331.93211 – reference: BillingsSANonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains2013ChichesterWiley10.1002/97811185355611287.93101 – reference: LiJParameter estimation for Hammerstein CARARMA systems based on the Newton iterationAppl Math Lett20132619196297140610.1016/j.aml.2012.03.0381255.65119 – reference: VörösJIdentification of nonlinear dynamic systems with input saturation and output backlash using three-block cascade modelsJ Franklin Inst20143511254555466327665010.1016/j.jfranklin.2014.09.025 – reference: ChenHDingFXiaoYDecomposition-based least squares parameter estimation algorithm for input nonlinear systems using the key term separation techniqueNonlinear Dyn20157932027203510.1007/s11071-014-1791-51331.93083 – reference: UgaldeHMRCarmonaJCReyes-ReyesJAlvaradoVMMantillaJComputational cost improvement of neural network models in black box nonlinear system identificationNeurocomputing20151669610810.1016/j.neucom.2015.04.022 – reference: ChenHXiaoYDingFHierarchical gradient parameter estimation algorithm for Hammerstein nonlinear systems using the key term separation principleAppl Math Comput20142471202121032709181343.62055 – reference: XuLApplication of the Newton iteration algorithm to the parameter estimation for dynamical systemsJ Comput Appl Math20152883343334960210.1016/j.cam.2015.03.0571314.93062 – reference: Al-SaggafUMMoinuddinMArifMZerguineAThe q-least mean squares algorithmSignal Process2015111506010.1016/j.sigpro.2014.11.016 – reference: XuLChenLXiongWParameter estimation and controller design for dynamic systems from the step responses based on the Newton iterationNonlinear Dyn201579321552163330542110.1007/s11071-014-1801-7 – reference: ElleuchKKharratMChaariAChaabaneMModeling and identification of block-oriented heat transfer processInt J Inf Syst Sci20095141561158.93408 – reference: RajaMAZChaudharyNIAdaptive strategies for parameter estimation of Box–Jenkins systemsIET Signal Process20148996898010.1049/iet-spr.2013.0438 – reference: WangYDingFRecursive least squares algorithm and gradient algorithm for Hammerstein–Wiener systems using the data filteringNonlinear Dyn201684210451053347494610.1007/s11071-015-2548-51354.93158 – reference: MaoYDingFA novel data filtering based multi-innovation stochastic gradient algorithm for Hammerstein nonlinear systemsDigit Signal Proc201546215225341171410.1016/j.dsp.2015.07.002 – reference: MoinuddinMAl-SaggafUMAhmedAFamily of state space least mean power of two-based algorithmsEURASIP J Adv Signal Process201520151110.1186/s13634-015-0219-9 – volume-title: Application of system identification in engineering year: 2014 ident: 2762_CR1 – volume: 10 start-page: 639 issue: 3 year: 2012 ident: 2762_CR47 publication-title: Int J Control Automat Syst doi: 10.1007/s12555-012-0322-8 – volume: 351 start-page: 5455 issue: 12 year: 2014 ident: 2762_CR20 publication-title: J Franklin Inst doi: 10.1016/j.jfranklin.2014.09.025 – volume: 58 start-page: 3054 issue: 12 year: 2013 ident: 2762_CR28 publication-title: IEEE Trans Automat Control doi: 10.1109/TAC.2013.2273283 – volume: 84 start-page: 1045 issue: 2 year: 2016 ident: 2762_CR14 publication-title: Nonlinear Dyn doi: 10.1007/s11071-015-2548-5 – volume: 111 start-page: 50 year: 2015 ident: 2762_CR58 publication-title: Signal Process doi: 10.1016/j.sigpro.2014.11.016 – year: 2016 ident: 2762_CR46 publication-title: Nonlinear Dyn doi: 10.1007/s11071-016-3058-9 – volume: 8 start-page: 968 issue: 9 year: 2014 ident: 2762_CR3 publication-title: IET Signal Process doi: 10.1049/iet-spr.2013.0438 – volume: 37 start-page: 1694 issue: 4 year: 2013 ident: 2762_CR35 publication-title: Appl Math Model doi: 10.1016/j.apm.2012.04.039 – volume: 247 start-page: 1202 year: 2014 ident: 2762_CR38 publication-title: Appl Math Comput doi: 10.1016/j.amc.2014.09.070 – volume: 39 start-page: 7145 issue: 10 year: 2014 ident: 2762_CR57 publication-title: Arab J Sci Eng doi: 10.1007/s13369-014-1289-y – volume: 41 start-page: 527 issue: 2 year: 2016 ident: 2762_CR59 publication-title: Arab J Sci Eng doi: 10.1007/s13369-015-1698-6 – volume-title: Nonlinear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains year: 2013 ident: 2762_CR27 doi: 10.1002/9781118535561 – volume: 35 start-page: 3323 issue: 9 year: 2016 ident: 2762_CR9 publication-title: Circuits Syst Signal Process doi: 10.1007/s00034-015-0190-6 – volume: 113 start-page: 273 year: 2015 ident: 2762_CR2 publication-title: Signal Process doi: 10.1016/j.sigpro.2015.02.010 – volume: 107 start-page: 327 year: 2015 ident: 2762_CR6 publication-title: Signal Process doi: 10.1016/j.sigpro.2014.06.015 – volume: 355 start-page: 237 year: 2016 ident: 2762_CR42 publication-title: Inf Sci doi: 10.1016/j.ins.2016.03.037 – volume: 33 start-page: 2881 issue: 9 year: 2014 ident: 2762_CR41 publication-title: Circuits Syst Signal Process doi: 10.1007/s00034-014-9772-y – volume: 34 start-page: 61 issue: 1 year: 2015 ident: 2762_CR49 publication-title: Circuits Syst Signal Process doi: 10.1007/s00034-014-9839-9 – volume: 288 start-page: 33 year: 2015 ident: 2762_CR10 publication-title: J Comput Appl Math doi: 10.1016/j.cam.2015.03.057 – volume: 26 start-page: 171 issue: 1 year: 2015 ident: 2762_CR7 publication-title: Neural Comput Appl doi: 10.1007/s00521-014-1716-8 – volume: 47 start-page: 493 issue: 3 year: 2014 ident: 2762_CR23 publication-title: IFAC Proc doi: 10.3182/20140824-6-ZA-1003.01754 – volume: 79 start-page: 1745 issue: 3 year: 2015 ident: 2762_CR39 publication-title: Nonlinear Dyn doi: 10.1007/s11071-014-1771-9 – ident: 2762_CR52 doi: 10.1155/2013/467276 – year: 2016 ident: 2762_CR51 publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2677-x – volume: 48 start-page: 392 issue: 28 year: 2015 ident: 2762_CR29 publication-title: IFAC Pap Online doi: 10.1016/j.ifacol.2015.12.159 – volume: 120 start-page: 660 year: 2016 ident: 2762_CR12 publication-title: Signal Process doi: 10.1016/j.sigpro.2015.10.009 – volume: 62 start-page: 131 issue: 1 year: 2014 ident: 2762_CR31 publication-title: IEEE Trans Signal Process doi: 10.1109/TSP.2013.2286103 – volume-title: Adaptive signal processing: theory and applications year: 2012 ident: 2762_CR54 – volume: 10 start-page: 894 issue: 8 year: 2016 ident: 2762_CR16 publication-title: IET Control Theory Appl doi: 10.1049/iet-cta.2015.1195 – ident: 2762_CR30 doi: 10.1007/s00521-016-2394-5 – volume: 5 start-page: 41 issue: 1 year: 2009 ident: 2762_CR25 publication-title: Int J Inf Syst Sci – volume: 56 start-page: 839 issue: 2 year: 2008 ident: 2762_CR55 publication-title: IEEE Trans Signal Process doi: 10.1109/TSP.2007.907805 – volume: 330 start-page: 1018 issue: 5 year: 2011 ident: 2762_CR26 publication-title: J Sound Vib doi: 10.1016/j.jsv.2010.09.012 – volume: 79 start-page: 2027 issue: 3 year: 2015 ident: 2762_CR50 publication-title: Nonlinear Dyn doi: 10.1007/s11071-014-1791-5 – volume: 76 start-page: 777 issue: 1 year: 2014 ident: 2762_CR48 publication-title: Nonlinear Dyn doi: 10.1007/s11071-013-1168-1 – volume: 23 start-page: 1952 issue: 5 year: 2015 ident: 2762_CR36 publication-title: IEEE Trans Control Syst Technol doi: 10.1109/TCST.2014.2387216 – volume: 222 start-page: 203 year: 2013 ident: 2762_CR37 publication-title: Inf Sci doi: 10.1016/j.ins.2012.07.064 – volume: 26 start-page: 294 year: 2012 ident: 2762_CR24 publication-title: Mech Syst Signal Process doi: 10.1016/j.ymssp.2011.06.010 – volume: 129 start-page: 223 year: 2015 ident: 2762_CR22 publication-title: Chem Eng Sci doi: 10.1016/j.ces.2015.02.021 – volume: 7 start-page: 176 issue: 2 year: 2013 ident: 2762_CR34 publication-title: IET Control Theory Appl doi: 10.1049/iet-cta.2012.0313 – volume: 21 start-page: 161 issue: 1 year: 2012 ident: 2762_CR5 publication-title: Neural Comput Appl doi: 10.1007/s00521-010-0461-x – volume: 79 start-page: 2187 issue: 3 year: 2015 ident: 2762_CR19 publication-title: Nonlinear Dyn doi: 10.1007/s11071-014-1804-4 – year: 2016 ident: 2762_CR18 publication-title: Neural Comput Appl doi: 10.1007/s00521-016-2548-5 – volume: 79 start-page: 1385 issue: 2 year: 2015 ident: 2762_CR44 publication-title: Nonlinear Dyn doi: 10.1007/s11071-014-1748-8 – volume-title: System identification: an introduction year: 2011 ident: 2762_CR17 doi: 10.1007/978-0-85729-522-4 – volume: 71 start-page: 308 year: 2016 ident: 2762_CR15 publication-title: Automatica doi: 10.1016/j.automatica.2016.05.024 – volume: 26 start-page: 91 issue: 1 year: 2013 ident: 2762_CR32 publication-title: Appl Math Lett doi: 10.1016/j.aml.2012.03.038 – volume: 82 start-page: 1811 issue: 4 year: 2015 ident: 2762_CR53 publication-title: Nonlinear Dyn doi: 10.1007/s11071-015-2279-7 – volume: 236 start-page: 391 year: 2014 ident: 2762_CR8 publication-title: Appl Math Comput doi: 10.1016/j.amc.2014.02.087 – volume: 166 start-page: 96 year: 2015 ident: 2762_CR4 publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.04.022 – volume: 116 start-page: 141 year: 2015 ident: 2762_CR45 publication-title: Signal Process doi: 10.1016/j.sigpro.2015.04.015 – ident: 2762_CR21 doi: 10.1007/978-1-84996-513-2_4 – year: 2017 ident: 2762_CR13 publication-title: Circuits Syst Signal Process doi: 10.1007/s00034-016-0378-4 – volume: 57 start-page: 13 year: 2016 ident: 2762_CR33 publication-title: Appl Math Lett doi: 10.1016/j.aml.2015.12.018 – volume: 60 start-page: 21 year: 2016 ident: 2762_CR43 publication-title: Appl Math Lett doi: 10.1016/j.aml.2016.03.016 – volume: 79 start-page: 2155 issue: 3 year: 2015 ident: 2762_CR11 publication-title: Nonlinear Dyn doi: 10.1007/s11071-014-1801-7 – volume: 48 start-page: 282 year: 2014 ident: 2762_CR60 publication-title: Measurement doi: 10.1016/j.measurement.2013.11.010 – volume: 2015 start-page: 1 issue: 1 year: 2015 ident: 2762_CR56 publication-title: EURASIP J Adv Signal Process doi: 10.1186/s13634-015-0219-9 – volume: 46 start-page: 215 year: 2015 ident: 2762_CR40 publication-title: Digit Signal Proc doi: 10.1016/j.dsp.2015.07.002 |
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| Title | Design of momentum LMS adaptive strategy for parameter estimation of Hammerstein controlled autoregressive systems |
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