Capobianco, E. (2002). Hammerstein system representation of financial volatility processes, The European Physical JournalB: Condensed Matter 27(2): 201-211.10.1140/epjb/e20020154
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
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
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
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
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
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
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
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
Greblicki, W. (2002). Stochastic approximation in nonparametric identification of Hammerstein systems, IEEE Transactions on Automatic Control47(11): 1800-1810.10.1109/TAC.2002.804483
Greblicki, W. (2004). Hammerstein system identification with stochastic approximation, International Journal of Modellingand Simulation 24(2): 131-138.10.1080/02286203.2004.11442297
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
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
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
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
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
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
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
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
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
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
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
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.
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