Data filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principle

Bai, 2002, A blind approach to the Hammerstein–Wiener model identification, Automatica, 38, 967, 10.1016/S0005-1098(01)00292-8 Chen, 2010, Auxiliary model based multi-innovation algorithms for multivariable nonlinear systems, Mathematical and Computer Modelling, 52, 1428, 10.1016/j.mcm.2010.05.026 da Silva, 2010, Nonlinear identification in structural dynamics based on Wiener series and Kautz filters, Mechanical Systems and Signal Processing, 24, 52, 10.1016/j.ymssp.2009.05.017 Ding, 2005, Identification of Hammerstein nonlinear ARMAX systems, Automatica, 41, 1479, 10.1016/j.automatica.2005.03.026 Ding, 2011, Identification methods for Hammerstein nonlinear systems, Digital Signal Processing, 21, 215, 10.1016/j.dsp.2010.06.006 Ding, 2010, Partially coupled stochastic gradient identification methods for non-uniformly sampled systems, IEEE Transactions on Automatic Control, 55, 1976, 10.1109/TAC.2010.2050713 Ding, 2009, Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems, Automatica, 45, 324, 10.1016/j.automatica.2008.08.007 Ding, 2006, Gradient-based identification methods for Hammerstein nonlinear ARMAX models, Nonlinear Dynamics, 45, 31, 10.1007/s11071-005-1850-z Ding, 2007, Auxiliary model-based least-squares identification methods for Hammerstein output-error systems, Systems and Letters, 56, 373, 10.1016/j.sysconle.2006.10.026 Gan, 2010, A locally linear RBF network-based state-dependent AR model for nonlinear time series modeling, Information Sciences, 180, 4370, 10.1016/j.ins.2010.07.012 Gerrit, 2007, A multi-variate Hammerstein model for processes with input directionality, Journal of Process Control, 17, 539, 10.1016/j.jprocont.2006.12.001 Goethals, 2005, Subspace identification of Hammerstein systems using least squares support vector machines, IEEE Transactions on Automatic Control, 50, 1509, 10.1109/TAC.2005.856647 Greblicki, 2002, Recursive identification of continuous-time Hammerstein systems, International Journal of Systems Science, 33, 969, 10.1080/0020772021000046270 Han, 2009, Multi-innovation stochastic gradient algorithms for multi-input multi-output systems, Digital Signal Processing, 19, 545, 10.1016/j.dsp.2008.12.002 Hong, 2008, Model selection approaches for non-linear system identification: a review, International Journal of Systems Science, 39, 925, 10.1080/00207720802083018 Kerschen, 2006, Past, present and future of nonlinear system identification in structural dynamics, Mechanical Systems and Signal Processing, 20, 505, 10.1016/j.ymssp.2005.04.008 Kiliç, 2007, Comparison of different strategies of utilizing fuzzy clustering in structure identification, Information Sciences, 177, 5153, 10.1016/j.ins.2007.06.030 Lima, 2007, Hybridizing mixtures of experts with support vector machines: investigation into nonlinear dynamic systems identification, Information Sciences, 177, 2049, 10.1016/j.ins.2007.01.009 Liu, 2007, Iterative identification of Hammerstein systems, Automatica, 43, 346, 10.1016/j.automatica.2006.09.004 Liu, 2009, An auxiliary model based recursive least squares parameter estimation algorithm for non-uniformly sampled multirate systems, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 223, 445, 10.1243/09596518JSCE686 Mehta, 2010, Identification of a class of Wiener and Hammerstein-type nonlinear processes with monotonic static gains, ISA Transactions, 49, 501, 10.1016/j.isatra.2010.04.006 Papadakis, 2010, Piecewise-linear approximation of non-linear models based on probabilistically/possibilistically interpreted intervals’ numbers, Information Sciences, 180, 5060, 10.1016/j.ins.2010.03.023 Rahrooh, 2009, Identification of nonlinear systems using NARMAX model, Nonlinear Analysis: Theory, Methods and Applications, 71, e1198, 10.1016/j.na.2009.01.150 Rebollo-Neira, 2009, Nonlinear non-extensive approach for identification of structured information, Physica A: Statistical Mechanics and its Applications, 388, 4703, 10.1016/j.physa.2009.08.003 Verhaegen, 1996, Identifying MIMO Hammerstein systems in the context of subspace model identification methods, International Journal of Control, 63, 331, 10.1080/00207179608921846 Vörös, 1997, Parameter identification of discontinuous Hammerstein systems, Automatica, 33, 1141, 10.1016/S0005-1098(97)00009-5 Vörös, 2003, Recursive identification of Hammerstein systems with discontinuous nonlinearities containing dead-zones, IEEE Transactions on Automatic Control, 48, 2203, 10.1109/TAC.2003.820146 Wang, 2010, Auxiliary model-based RELS and MI-ELS algorithms for Hammerstein OEMA systems, Computers and Mathematics with Applications, 59, 3092, 10.1016/j.camwa.2010.02.030 Wang, 2010, Auxiliary model-based recursive generalized least squares parameter estimation for Hammerstein OEAR systems, Mathematical and Computer Modelling, 52, 309, 10.1016/j.mcm.2010.03.002 Wang, 2010, Input–output data filtering based recursive least squares identification for CARARMA systems, Digital Signal Processing, 20, 991, 10.1016/j.dsp.2009.12.006 Wang, 2008, Extended stochastic gradient identification algorithms for Hammerstein–Wiener ARMAX systems, Computers and Mathematics with Applications, 56, 3157, 10.1016/j.camwa.2008.07.015 Xie, 2010, Modeling and identification for non-uniformly periodically sampled-data systems, IET Control Theory and Applications, 4, 784, 10.1049/iet-cta.2009.0064 Zhang, 2009, Self-tuning control based on multi-innovation stochastic gradient parameter estimation, Systems and Control Letters, 58, 69, 10.1016/j.sysconle.2008.08.005