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Języki publikacji
Abstrakty
A simple semi-recursive routine for nonlinearity recovery in Hammerstein systems is proposed. The identification scheme is based on the Haar wavelet kernel and possesses a simple and compact form. The convergence of the algorithm is established and the asymptotic rate of convergence (independent of the input density smoothness) is shown for piecewise-Lipschitz nonlinearities. The numerical stability of the algorithm is verified. Simulation experiments for a small and moderate number of input-output data are presented and discussed to illustrate the applicability of the routine.
Rocznik
Tom
Strony
507--520
Opis fizyczny
Bibliogr. 50 poz., rys., tab., wykr.
Twórcy
autor
- Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, Wrocław, Poland
autor
- Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, Wrocław, Poland
autor
- Institute of Computer Engineering, Control and Robotics, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, Wrocław, Poland
Bibliografia
- [1] Capobianco, E. (2002). Hammerstein system representation of financial volatility processes, The European Physical Journal B: Condensed Matter 27(2): 201–211.
- [2] Chen, H.-F. (2004). Pathwise convergence of recursive identification algorithms for Hammerstein systems, IEEE Transactions on Automatic Control 49(10): 1641–1649.
- [3] 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.
- [4] Chen, S., Billings, S.A. and Luo, W. (1989). Orthogonal least squares methods and their application to non-linear system identification, International Journal of Control 50(5): 1873–1896.
- [5] 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, International Journal of Applied Mathematics and Computer Science 21(3): 423–430, DOI: 10.2478/v10006-011-0031-0.
- [6] 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, IEEE Transactions on Biomedical Engineering 59(1): 205–212.
- [7] Coca, D. and Billings, S.A. (2001). Non-linear system identification using wavelet multiresolution models, International Journal of Control 74(18): 1718–1736.
- [8] Gallman, P. (1975). An iterative method for the identification of nonlinear systems using a Uryson model, IEEE Transactions on Automatic Control 20(6): 771–775.
- [9] Gomes, S.M. and Cortina, E. (1995). Some results on the convergence of sampling series based on convolution integrals, SIAM Journal on Mathematical Analysis 26(5): 1386–1402.
- [10] Greblicki, W. (2002). Stochastic approximation in nonparametric identification of Hammerstein systems, IEEE Transactions on Automatic Control 47(11): 1800–1810.
- [11] Greblicki, W. (2004). Hammerstein system identification with stochastic approximation, International Journal of Modelling and Simulation 24(2): 131–138.
- [12] Greblicki, W. and Pawlak, M. (1986). Identification of discrete Hammerstein system using kernel regression estimates, IEEE Transactions on Automatic Control 31(1): 74–77.
- [13] 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.
- [14] Greblicki, W. and Pawlak, M. (1989). Recursive nonparametric identification of Hammerstein systems, Journal of the Franklin Institute 326(4): 461–481.
- [15] Greblicki, W. and Pawlak, M. (2008). Nonparametric System Identification, Cambridge University Press, New York, NY.
- [16] Györfi, L., Kohler, M., Krzyżak, A. and Walk, H. (2002). A Distribution-Free Theory of Nonparametric Regression, Springer-Verlag, New York, NY.
- [17] Hasiewicz, Z. (1999). Hammerstein system identification by the Haar multiresolution approximation, International Journal of Adaptive Control and Signal Processing 13(8): 697–717.
- [18] Hasiewicz, Z. (2000). Modular neural networks for non-linearity recovering by the Haar approximation, Neural Networks 13(10): 1107–1133.
- [19] 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 and Systems I: Regular Papers 52(1): 427–442.
- [20] 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 Science 33(14): 1121–1144.
- [21] Jyothi, S.N. and Chidambaram, M. (2000). Identification of Hammerstein model for bioreactors with input multiplicities, Bioprocess Engineering 23(4): 323–326.
- [22] Krzyżak, A. (1986). The rates of convergence of kernel regression estimates and classification rules, IEEE Transactions on Information Theory 32(5): 668–679.
- [23] Krzyżak, A. (1992). Global convergence of the recursive kernel regression estimates with applications in classification and nonlinear system estimation, IEEE Transactions on Information Theory 38(4): 1323–1338.
- [24] Krzyżak, A. (1993). Identification of nonlinear block-oriented systems by the recursive kernel estimate, Journal of the Franklin Institute 330(3): 605–627.
- [25] Krzyżak, A. and Pawlak, M. (1984). Distribution-free consistency of a nonparametric kernel regression estimate and classification, IEEE Transactions on Information Theory 30(1): 78–81.
- [26] Kukreja, S., Kearney, R. and Galiana, H. (2005). A least-squares parameter estimation algorithm for switched Hammerstein systems with applications to the VOR, IEEE Transactions on Biomedical Engineering 52(3): 431–444.
- [27] Kushner, H.J. and Yin, G.G. (2003). Stochastic Approximation and Recursive Algorithms and Applications, 2nd Edn., Stochastic Modelling and Applied Probability, Springer, New York, NY.
- [28] Lortie, M. and Kearney, R.E. (2001). Identification of time-varying Hammerstein systems from ensemble data, Annals of Biomedical Engineering 29(2): 619–635.
- [29] Mallat, S.G. (1998). A Wavelet Tour of Signal Processing, Academic Press, San Diego, CA.
- [30] Marmarelis, V.Z. (2004). Nonlinear Dynamic Modeling of Physiological Systems, IEEE Press Series on Biomedical Engineering, Wiley-IEEE Press, Piscataway, NJ.
- [31] 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.
- [32] Patan, K. and Korbicz, J. (2012). Nonlinear model predictive control of a boiler unit: A fault tolerant control study, International Journal of Applied Mathematics and Computer Science 22(1): 225–237, DOI: 10.2478/v10006-012-0017-6.
- [33] Pawlak, M. and Hasiewicz, Z. (1998). Nonlinear system identification by the Haar multiresolution analysis, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications 45(9): 945–961.
- [34] Pawlak, M., Rafajłowicz, E. and Krzyżak, A. (2003). Postfiltering versus prefiltering for signal recovery from noisy samples, IEEE Transactions on Information Theory 49(12): 3195–3212.
- [35] Rutkowski, L. (1984). On nonparametric identification with prediction of time-varying systems, IEEE Transactions on Automatic Control 29(1): 58–60.
- [36] Rutkowski, L. (2004). Generalized regression neural networks in time-varying environment, IEEE Transactions on Neural Networks 15(3): 576–596.
- [37] Saeedi, H., Mollahasani, N., Moghadam, M.M. and Chuev, G.N. (2011). An operational Haar wavelet method for solving fractional Volterra integral equations, International Journal of Applied Mathematics and Computer Science 21(3): 535–547, DOI: 10.2478/v10006-011-0042-x.
- [38] Sansone, G. (1959). Orthogonal Functions, Interscience, New York, NY.
- [39] Serfling, R.J. (1980). Approximation Theorems of Mathematical Statistics, Wiley, New York, NY.
- [40] Skubalska-Rafajłowicz, E. (2001). Pattern recognition algorithms based on space-filling curves and orthogonal expansions, IEEE Transactions on Information Theory 47(5): 1915–1927.
- [41] Śliwiński, P. (2010). On-line wavelet estimation of Hammerstein system nonlinearity, International Journal of Applied Mathematics and Computer Science 20(3): 513–523, DOI: 10.2478/v10006-010-0038-y.
- [42] Śliwiński, P. (2013). Nonlinear System Identification by Haar Wavelets, Lecture Notes in Statistics, Vol. 210, Springer-Verlag, Heidelberg.
- [43] Stone, C.J. (1980). Optimal rates of convergence for nonparametric regression, Annals of Statistics 8(6): 1348–1360.
- [44] Szego, G. (1974). Orthogonal Polynomials, 3rd Edn., American Mathematical Society, Providence, RI.
- [45] Van der Vaart, A. (2000). Asymptotic Statistics, Cambridge University Press, Cambridge.
- [46] Vörös, J. (2003). Recursive identification of Hammerstein systems with discontinuous nonlinearities containing dead-zones, IEEE Transactions on Automatic Control 48(12): 2203–2206.
- [47] Walter, G.G. and Shen, X. (2001). Wavelets and Other Orthogonal Systems With Applications, 2nd Edn., Chapman & Hall, Boca Raton, FL.
- [48] Westwick, D.T. and Kearney, R.E. (2003). Identification of Nonlinear Physiological Systems, IEEE Press Series on Biomedical Engineering, Wiley-IEEE Press, Piscataway, NJ.
- [49] 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.
- [50] Zhou, D. and De Brunner, V.E. (2007). Novel adaptive nonlinear predistorters based on the direct learning algorithm, IEEE Transactions on Signal Processing 55(1): 120–133.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0431e8a7-03f7-48aa-875f-76c33c7dec8e