System Identification

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[2] R. K. Pearson. Selecting nonlinear model structures for computer control. Journal of Process Control, 13:1-26, 2003.
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[3] L. A. Aguirre, C. Letellier, and J. Maquet. Induced one-parameter bifurcations in identified nonlinear models. International Journal of Bifurcation and Chaos, 12(1):135-145, 2002.
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[8] L. A. Aguirre. Introduçao à Identificaçao de Sistemas. Editora da UFMG, 2000.
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[9] M. V. Corrêa, L. A. Aguirre, and E. M. A. M. Mendes. Modeling chaotic dynamics with discrete nonlinear rational models. International Journal of Bifurcation and Chaos, 10(5):1019-1032, 2000.
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[10] T. A. Johansen. Multi-objective identification of FIR models. In Proceedings of 12th IFAC Symposium on System Identification 2000, Santa Barbara, USA, 2000.
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[11] J. H. Lee. Nonlinear Model Predictive Control, volume 26 of Progress in Systems and Control Theory Series, chapter Modeling for nonlinear model predictive control: requirements, current status and future research needs. Birkhauser Verlag, Basel, 2000.
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[12] R. K. Pearson and M. Pottmann. Gray-box identification of block-oriented nonlinear models. Journal of Process Control, 10:301-315, 2000.
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[18] A. Aguirre and L. A. Aguirre. Time series analysis of monthly beef cattle prices with nonlinear autoregressive models. Applied Economics, 32:245-275, 1998.
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[19] L. A. Aguirre and A. V. P. Souza. An algorithm for estimating fixed points of Dynamical Systems from time series. International Journal of Bifurcation and Chaos, 8(11):2203-2213, 1998.
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[20] F. Lorito. Identification of a grey-box model of nonlinear current transformer for simulations purposes. Control Engineering Practice, 6:1331-1339, 1998.
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[21] L. A Aguirre. On the structure of nonlinear polynomial models: higher order correlation functions, spectra, and term clusters. IEEE Transactions on Circuits and Systems Part I: Fundamental Theory and Applications, 44(5):450-453, 1997.
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[22] A. Alessandri and T. Parisini. Nonlinear modelling of complex large-scale plants using neural networks and stochastic approximation. IEEE Transactions on Systems, and Cybernetics - Part A, 27(6):750-757, 1997.
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[23] F. A. Cubillos and E. L. Lima. Identification and optimizing control of a roucher flotation circuit using an adaptable hybrid-neural model. Minerals Engineering, 10(7):707-721, 1997.
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[24] U. Forssell and P. Lindskog. Combining semi-physical and neural network modeling: an example of its usefulness. In Proceedings of the 11th IFAC Symposium on System Identification, volume 4, pages 795-798, Kitakyushu, Fukuoka, Japan, 1997.
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[25] J. Funkquist. Grey-box identification of a continuous digester - a distributed-parameter process. Control Engineering Practice, 5(7):919-930, 1997.
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[26] C. Garcia. Modelagem e simulaçao de processos industriais e de sistemas eletromecânicos. EDUSP, Sao Paulo, 1997.
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[27] T. Johansen. Constrained and regularized system identification. In Proceedings of the 11th IFAC Symposium on System Identification, Kitakyushu, Fukuoda, Japan, 1997.
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[31] F. A. Cubillos, P. I. Alvarez, J. C. Pinto, and E. L. Lima. Hybrid-neural modeling for particulate solid drying processes. Power Technology, 87:153-160, 1996.
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[33] A. K. Swain. Continuous time identification of nonlinear systems using frequency domain transfer functions. PhD thesis, University of Sheffield, Sheffield, UK, 1996.
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[34] L. A. Aguirre. A nonlinear correlation function for selecting the delay time in dynamical reconstructions. Physics Letters A, 203(2,3):88-94, 1995.
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[35] L. A. Aguirre and S. A. Billings. Improved structure selection for nonlinear models based on term clustering. International Journal of Control, 62(3):569-587, 1995.
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[36] S. B. Jorgensen and K. M. Hangos. Grey box modelling for control: qualitative models as a unifying framework. International Journal of Adaptative Control and Signal Processing, 9(6):547-562, 1995.
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[38] Miroslav Kárny, Petr Nedoma, Josef Böhm, and Alena Halousková. Approximate ARX model estimation for jacketing adaptive systems. In Banyasz Cs, editor, Adaptive Systems in Control and Signal Processing. Preprints, pages 129-134, Budapest, 1995. IFAC.
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[40] P. Lindskog and L. Ljung. Tools for semiphysical modelling. International Journal of Adaptative Control and Signal Processing, 9(6):509, 1995.
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[42] M. L. Thompson and M. A. Kramer. Modeling chemical processes using prior knowledge and neural networks. AIChE Journal, 40(8):1328-1340, August 1994.
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[43] L. A. Aguirre. Some remarks on structure selection for nonlinear models. International Journal of Bifurcation and Chaos, 4:1707-1714, 1994.
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[44] L. A. Aguirre and S. A. Billings. Validating identified nonlinear models with chaotic dynamics. International Journal of Bifurcation and Chaos, 4(1):109-125, 1994.
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[45] T. Bohlin. A case study of grey box identification. Automatica, 30(2):307-318, 1994.
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[46] Marco A. Duran and B. S. White. Bayesian estimation applied to effective heat transfer coefficients in a packed bed. Chemical Engineering Science, 50(3):495-510, 1994.
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[47] E. Eskinat, S. H. Johnson, and W. L. Luyben. Use of auxiliary information in system identification. Industrial & Engineering Chemistry Researc, 32:1981-1992, 1993.
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[48] B. M. Ninness. Stochastic and deterministic modelling. PhD thesis, The University of New Castle, Australia, 1993.
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[50] S. Chen and S. A. Billings. Representations of non-linear systems: the NARMAX model. International Journal of Control, 49(3):1013-1032, 1989.
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[51] I. J. Leontaritis and S. A. Billings. Input-output parametric models for non-linear systems - part i: deterministic non-linear systems. International Journal of Control, 41(2):303-328, 1985.
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[52] I. J. Leontaritis and S. A. Billings. Input-output parametric models for non-linear systems - part ii: sthocastic non-linear systems. International Journal of Control, 41(2):329-344, 1985.
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by Erivelton Geraldo Nepomuceno in Aug 2005