[
[1] K. Hunt, M. Munih, N. Donaldson, F. Barr, Investigation of the Hammerstein hypothesis in the modeling of electrically stimulated muscle, IEEE Transactions on Biomedical Engineering 45 (1998) 998–1009.10.1109/10.7048689691574
]Search in Google Scholar
[
[2] T. Kara, I. Eker, Nonlinear modeling and identification of a DC motor for bidirectional operation with real time experiments, Energy Conversion and Management 45 (2004) 1087–1106.10.1016/j.enconman.2003.08.005
]Search in Google Scholar
[
[3] T. Quatieri, D. Reynolds, G. O’Leary, Estimation of handset nonlinearity with application to speaker recognition, IEEE Transactions on Speech and Audio Processing 8 (2000) 567–584.10.1109/89.861376
]Search in Google Scholar
[
[4] J. Turunen, J. Tanttu, P. Loula, Hammerstein model for speech coding, EURASIP Journal on Applied Signal Processing (2003) 1238–1249.10.1155/S1110865703307048
]Search in Google Scholar
[
[5] E. Capobianco, Hammerstein system representation of financial volatility processes, The European Physical Journal B 27 (2002) 201–211.
]Search in Google Scholar
[
[6] W. Greblicki, M. Pawlak, Nonparametric System Identification, Cambridge University Press, 2008.10.1017/CBO9780511536687
]Search in Google Scholar
[
[7] L. Ljung, System Identification: Theory for the User, Prentice Hall, New Jersey, 1987.
]Search in Google Scholar
[
[8] F. Giri, E. Bai, Block-Oriented Nonlinear System Identification, Springer-Verlag, 2010.10.1007/978-1-84996-513-2
]Search in Google Scholar
[
[9] H.-F. Chen, W. Zhao, Recursive Identification and Parameter Estimation, CRC Press, 2014.10.1201/b17078
]Search in Google Scholar
[
[10] R. Pintelon, J. Schoukens, System Identification: A Frequency Domain Approach, John Wiley & Sons, 2012.10.1002/9781118287422
]Search in Google Scholar
[
[11] S. A. Billings, Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains, John Wiley & Sons, 2013.10.1002/9781118535561
]Search in Google Scholar
[
[12] W. Greblicki, M. Pawlak, The weighted nearest neighbor estimate for Hammerstein system identification, IEEE Transactions on Automatic Control 64 (2019) 1550–1565.10.1109/TAC.2018.2866463
]Search in Google Scholar
[
[13] W. Greblicki, M. Pawlak, Hammerstein system identification with the nearest neighbor algorithm, IEEE Transactions on Information Theory 63 (2017) 4746–4757.10.1109/TIT.2017.2694013
]Search in Google Scholar
[
[14] J. G. Smith, S. Kamat, K. Madhavan, Modeling of ph process using wavenet based Hammerstein model, Journal of Process Control 17 (6) (2007) 551–561.10.1016/j.jprocont.2006.11.001
]Search in Google Scholar
[
[15] K. Fruzzetti, A. Palazoğlu, K. McDonald, Nolinear model predictive control using Hammerstein models, Journal of Process Control 7 (1) (1997) 31–41.10.1016/S0959-1524(97)80001-B
]Search in Google Scholar
[
[16] L. Jia, X. Li, M.-S. Chiu, Correlation analysis based mimo neuro-fuzzy Hammerstein model with noises, Journal of Process Control 41 (2016) 76–91.10.1016/j.jprocont.2015.11.006
]Search in Google Scholar
[
[17] J. Wang, Q. Zhang, Detection of asymmetric control valve stiction from oscillatory data using an extended Hammerstein system identification method, Journal of Process Control 24 (1) (2014) 1–12.10.1016/j.jprocont.2013.10.012
]Search in Google Scholar
[
[18] W. Wu, D.-W. Jhao, Control of a direct internal reforming molten carbonate fuel cell system using wavelet network-based Hammerstein models, Journal of Process Control 22 (3) (2012) 653–658.10.1016/j.jprocont.2012.01.011
]Search in Google Scholar
[
[19] C. Qi, H.-T. Zhang, H.-X. Li, A multi-channel spatio-temporal Hammerstein modeling approach for nonlinear distributed parameter processes, Journal of Process Control 19 (1) (2009) 85–99.10.1016/j.jprocont.2008.01.006
]Search in Google Scholar
[
[20] G. Harnischmacher, W. Marquardt, A multi-variate Hammerstein model for processes with input directionality, Journal of Process Control 17 (2007) 539–550.10.1016/j.jprocont.2006.12.001
]Search in Google Scholar
[
[21] M. Pawlak, On the series expansion approach to the identification of Hammerstein systems, IEEE Transactions on Automatic Control 36 (6) (1991) 763–767.10.1109/9.86954
]Search in Google Scholar
[
[22] D. Specht, A general regression neural network, IEEE Transactions on Neural Networks 2 (1991) 568–576.10.1109/72.9793418282872
]Search in Google Scholar
[
[23] P. Duda, M. Jaworski, L. Rutkowski, Convergent time-varying regression models for data streams: Tracking concept drift by the recursive Parzen-based generalized regression neural networks, International Journal of Neural Systems 28 (2018) 1750048.
]Search in Google Scholar
[
[24] J. S. Marron, Automatic smoothing parameter selection: a survey, Empirical Economics 13 (3-4) (1988) 187–208.10.1007/BF01972448
]Search in Google Scholar
[
[25] W. Härdle, P. Hall, J. S. Marron, How far are automatically chosen regression smoothing parameters from their optimum?, Journal of the American Statistical Association 83 (401) (1988) 86–95.10.1080/01621459.1988.10478568
]Search in Google Scholar
[
[26] J. Rice, Bandwidth choice for nonparametric regression, The Annals of Statistics (1984) 1215–1230.10.1214/aos/1176346788
]Search in Google Scholar
[
[27] T. Gasser, A. Kneip, W. Köhler, A flexible and fast method for automatic smoothing, Journal of the American Statistical Association 86 (415) (1991) 643–652.10.1080/01621459.1991.10475090
]Search in Google Scholar
[
[28] J. S. Marron, Partitioned cross-validation, Econometric Reviews 6 (2) (1987) 271–283.10.1080/07474938708800136
]Search in Google Scholar
[
[29] C.-K. Chu, J. S. Marron, Comparison of two bandwidth selectors with dependent errors, The Annals of Statistics (1991) 1906–1918.10.1214/aos/1176348377
]Search in Google Scholar
[
[30] N. Altman, Kernel smoothing of data with correlated errors, Journal of the American Statistical Association 85 (411) (1990) 749–759.10.1080/01621459.1990.10474936
]Search in Google Scholar
[
[31] J. Hart, P. Vieu, Data-driven bandwidth choice for density estimation based on dependent data, The Annals of Statistics 18 (2) (1990) 873–890.10.1214/aos/1176347630
]Search in Google Scholar
[
[32] K. D. Brabanter, J. D. Brabanter, J. Suykens, Kernel regression in the presence of correlated errors, The Journal of Machine Learning Research 12 (2011) 1955–1976.
]Search in Google Scholar
[
[33] Q. Yao, H. Tong, Cross-validatory bandwidth selections for regression estimation based on dependent data, Journal of Statistical Planning and Inference 68 (2) (1998) 387–415.10.1016/S0378-3758(97)00151-1
]Search in Google Scholar
[
[34] A. Quintela del Río, Comparison of bandwidth selectors in nonparametric regression under dependence, Computational Statistics & Data Analysis 21 (5) (1996) 563–580.10.1016/0167-9473(95)00028-3
]Search in Google Scholar
[
[35] I. Goethals, K. Pelckmans, J. Suykens, B. De Moor, Identification of MIMO Hammerstein models using least squares support vector machines, Automatica 41 (2005) 1263–1272.10.1016/j.automatica.2005.02.002
]Search in Google Scholar
