[Capobianco, E. (2002). Hammerstein system representation of financial volatility processes, The European Physical JournalB: Condensed Matter 27(2): 201-211.10.1140/epjb/e20020154]Search in Google Scholar
[Chen, H.-F. (2004). Pathwise convergence of recursive identification algorithms for Hammerstein systems, IEEETransactions on Automatic Control 49(10): 1641-1649.10.1109/TAC.2004.835358]Search in Google Scholar
[Chen, H.-F. (2010). Recursive identification for stochastic Hammerstein systems, in F. Giri and E.W. Bai (Eds.), Block-oriented Nonlinear System Identification, Lecture Notes in Control and Information Sciences, Vol. 404, Springer-Verlag, Berlin/Heidelberg, pp. 69-87.10.1007/978-1-84996-513-2_6]Search in Google Scholar
[Chen, S., Billings, S.A. and Luo, W. (1989). Orthogonal least squares methods and their application to non-linear system identification, International Journal of Control50(5): 1873-1896.10.1080/00207178908953472]Search in Google Scholar
[Chen, W., Khan, A.Q., Abid, M. and Ding, S.X. (2011). Integrated design of observer based fault detection for a class of uncertain nonlinear systems, InternationalJournal of Applied Mathematics and Computer Science21(3): 423-430, DOI: 10.2478/v10006-011-0031-0.10.2478/v10006-011-0031-0]Search in Google Scholar
[Clancy, E.A., Liu, L., Liu, P. and Moyer, D.V.Z. (2012). Identification of constant-posture EMG-torque relationship about the elbow using nonlinear dynamic models, IEEETransactions on Biomedical Engineering 59(1): 205-212.10.1109/TBME.2011.217042321968709]Search in Google Scholar
[Coca, D. and Billings, S.A. (2001). Non-linear system identification using wavelet multiresolution models, InternationalJournal of Control 74(18): 1718-1736.10.1080/00207170110089743]Search in Google Scholar
[Gallman, P. (1975). An iterative method for the identification of nonlinear systems using a Uryson model, IEEE Transactionson Automatic Control 20(6): 771-775.10.1109/TAC.1975.1101087]Search in Google Scholar
[Gomes, S.M. and Cortina, E. (1995). Some results on the convergence of sampling series based on convolution integrals, SIAM Journal on Mathematical Analysis26(5): 1386-1402.10.1137/S1052623493255096]Search in Google Scholar
[Greblicki, W. (2002). Stochastic approximation in nonparametric identification of Hammerstein systems, IEEE Transactions on Automatic Control47(11): 1800-1810.10.1109/TAC.2002.804483]Search in Google Scholar
[Greblicki, W. (2004). Hammerstein system identification with stochastic approximation, International Journal of Modellingand Simulation 24(2): 131-138.10.1080/02286203.2004.11442297]Search in Google Scholar
[Greblicki, W. and Pawlak, M. (1986). Identification of discrete Hammerstein system using kernel regression estimates, IEEE Transactions on Automatic Control 31(1): 74-77.10.1109/TAC.1986.1104096]Search in Google Scholar
[Greblicki, W. and Pawlak, M. (1987). Necessary and sufficient consistency conditions for a recursive kernel regression estimate, Journal of Multivariate Analysis 23(1): 67-76.10.1016/0047-259X(87)90178-3]Search in Google Scholar
[Greblicki, W. and Pawlak, M. (1989). Recursive nonparametric identification of Hammerstein systems, Journal of theFranklin Institute 326(4): 461-481.10.1016/0016-0032(89)90045-8]Search in Google Scholar
[Greblicki, W. and Pawlak, M. (2008). Nonparametric SystemIdentification, Cambridge University Press, New York, NY.10.1017/CBO9780511536687]Search in Google Scholar
[Györfi, L., Kohler, M., Krzyżak, A. and Walk, H. (2002). A Distribution-Free Theory of Nonparametric Regression, Springer-Verlag, New York, NY.10.1007/b97848]Search in Google Scholar
[Hasiewicz, Z. (1999). Hammerstein system identification by the Haar multiresolution approximation, International Journalof Adaptive Control and Signal Processing 13(8): 697-717.10.1002/(SICI)1099-1115(199912)13:8<691::AID-ACS591>3.0.CO;2-7]Search in Google Scholar
[Hasiewicz, Z. (2000). Modular neural networks for non-linearity recovering by the Haar approximation, Neural Networks13(10): 1107-1133.10.1016/S0893-6080(00)00055-1]Search in Google Scholar
[Hasiewicz, Z., Pawlak, M. and Śliwiński, P. (2005). Non-parametric identification of non-linearities in block-oriented complex systems by orthogonal wavelets with compact support, IEEE Transactions on Circuits andSystems I: Regular Papers 52(1): 427-442.10.1109/TCSI.2004.840288]Search in Google Scholar
[Hasiewicz, Z. and Śliwiński, P. (2002). Identification of non-linear characteristics of a class of block-oriented non-linear systems via Daubechies wavelet-based models, International Journal of Systems Science33(14): 1121-1144.10.1080/0020772021000064171]Search in Google Scholar
[Jyothi, S.N. and Chidambaram, M. (2000). Identification of Hammerstein model for bioreactors with input multiplicities, Bioprocess Engineering 23(4): 323-326.10.1007/s004499900141]Search in Google Scholar
[Krzyżak, A. (1986). The rates of convergence of kernel regression estimates and classification rules, IEEE Transactionson Information Theory 32(5): 668-679.10.1109/TIT.1986.1057226]Search in Google Scholar
[Krzyżak, A. (1992). Global convergence of the recursive kernel regression estimates with applications in classification and nonlinear system estimation, IEEE Transactions on InformationTheory 38(4): 1323-1338.10.1109/18.144711]Search in Google Scholar
[Krzyżak, A. (1993). Identification of nonlinear block-oriented systems by the recursive kernel estimate, Journal of theFranklin Institute 330(3): 605-627.10.1016/0016-0032(93)90101-Y]Search in Google Scholar
[Krzyżak, A. and Pawlak, M. (1984). Distribution-free consistency of a nonparametric kernel regression estimate and classification, IEEE Transactions on Information Theory30(1): 78-81.10.1109/TIT.1984.1056842]Search in Google Scholar
[Kukreja, S., Kearney, R. and Galiana, H. (2005). A least-squares parameter estimation algorithm for switched Hammerstein systems with applications to the VOR, IEEE Transactionson Biomedical Engineering 52(3): 431-444.10.1109/TBME.2004.84328615759573]Search in Google Scholar
[Kushner, H.J. and Yin, G.G. (2003). Stochastic Approximationand Recursive Algorithms and Applications, 2nd Edn., Stochastic Modelling and Applied Probability, Springer, New York, NY.]Search in Google Scholar
[Lortie, M. and Kearney, R.E. (2001). Identification of time-varying Hammerstein systems from ensemble data, Annals of Biomedical Engineering 29(2): 619-635.10.1114/1.138042111501626]Search in Google Scholar
[Mallat, S.G. (1998). A Wavelet Tour of Signal Processing, Academic Press, San Diego, CA.10.1016/B978-012466606-1/50008-8]Search in Google Scholar
[Marmarelis, V.Z. (2004). Nonlinear Dynamic Modeling ofPhysiological Systems, IEEE Press Series on Biomedical Engineering, Wiley-IEEE Press, Piscataway, NJ.10.1002/9780471679370]Search in Google Scholar
[Nordsjo, A. and Zetterberg, L. (2001). Identification of certain time-varying nonlinear Wiener and Hammerstein systems, IEEE Transactions on Signal Processing 49(3): 577-592.10.1109/78.905884]Search in Google Scholar
[Patan, K. and Korbicz, J. (2012). Nonlinear model predictive control of a boiler unit: A fault tolerant control study, International Journal of Applied Mathematicsand Computer Science 22(1): 225-237, DOI: 10.2478/v10006-012-0017-6.10.2478/v10006-012-0017-6]Search in Google Scholar
[Pawlak, M. and Hasiewicz, Z. (1998). Nonlinear system identification by the Haar multiresolution analysis, IEEETransactions on Circuits and Systems I: Fundamental Theoryand Applications 45(9): 945-961.10.1109/81.721260]Search in Google Scholar
[Pawlak, M., Rafajłowicz, E. and Krzyżak, A. (2003). Postfiltering versus prefiltering for signal recovery from noisy samples, IEEE Transactions on Information Theory49(12): 3195-3212.10.1109/TIT.2003.820013]Search in Google Scholar
[Rutkowski, L. (1984). On nonparametric identification with prediction of time-varying systems, IEEE Transactions onAutomatic Control 29(1): 58-60.10.1109/TAC.1984.1103377]Search in Google Scholar
[Rutkowski, L. (2004). Generalized regression neural networks in time-varying environment, IEEE Transactions on NeuralNetworks 15(3): 576 - 596.10.1109/TNN.2004.82612715384547]Search in Google Scholar
[Saeedi, H., Mollahasani, N., Moghadam, M.M. and Chuev, G.N. (2011). An operational Haar wavelet method for solving fractional Volterra integral equations, InternationalJournal of Applied Mathematics and Computer Science21(3): 535-547, DOI: 10.2478/v10006-011-0042-x.10.2478/v10006-011-0042-x]Search in Google Scholar
[Sansone, G. (1959). Orthogonal Functions, Interscience, New York, NY.]Search in Google Scholar
[Serfling, R.J. (1980). Approximation Theorems of MathematicalStatistics, Wiley, New York, NY.10.1002/9780470316481]Search in Google Scholar
[Skubalska-Rafajłowicz, E. (2001). Pattern recognition algorithms based on space-filling curves and orthogonal expansions, IEEE Transactions on Information Theory47(5): 1915-1927. 10.1109/18.930927]Search in Google Scholar
[Śliwiński, P. (2010). On-line wavelet estimation of Hammerstein system nonlinearity, International Journal of AppliedMathematics and Computer Science 20(3): 513-523, DOI: 10.2478/v10006-010-0038-y. 10.2478/v10006-010-0038-y]Search in Google Scholar
[Śliwiński, P. (2013). Nonlinear System Identification byHaar Wavelets, Lecture Notes in Statistics, Vol. 210, Springer-Verlag, Heidelberg.]Search in Google Scholar
[Stone, C.J. (1980). Optimal rates of convergence for nonparametric regression, Annals of Statistics8(6): 1348-1360.10.1214/aos/1176345206]Search in Google Scholar
[Szego, G. (1974). Orthogonal Polynomials, 3rd Edn., American Mathematical Society, Providence, RI.]Search in Google Scholar
[Van der Vaart, A. (2000). Asymptotic Statistics, Cambridge University Press, Cambridge.]Search in Google Scholar
[Vörös, J. (2003). Recursive identification of Hammerstein systems with discontinuous nonlinearities containing dead-zones, IEEE Transactions on Automatic Control48(12): 2203-2206.10.1109/TAC.2003.820146]Search in Google Scholar
[Walter, G.G. and Shen, X. (2001). Wavelets and Other OrthogonalSystems With Applications, 2nd Edn., Chapman & Hall, Boca Raton, FL.]Search in Google Scholar
[Westwick, D.T. and Kearney, R.E. (2003). Identification ofNonlinear Physiological Systems, IEEE Press Series on Biomedical Engineering, Wiley-IEEE Press, Piscataway, NJ.]Search in Google Scholar
[Wheeden, R. L. and Zygmund, A. (1977). Measure and Integral:An Introduction to Real Analysis, Pure and Applied Mathematics, Marcel Dekker Inc., New York, NY.]Search in Google Scholar
[Zhou, D. and DeBrunner, V.E. (2007). Novel adaptive nonlinear predistorters based on the direct learning algorithm, IEEETransactions on Signal Processing 55(1): 120-133. 10.1109/TSP.2006.882058]Search in Google Scholar