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Tytuł artykułu

MULTI-EDIP - an intelligent software package for computer-aided multivariate signal and system identification

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In the paper an intelligent software package MULTI-EDIP for computer-aided identification of multivariate signals and systems is presented. Purposes and main requirements for computer-aided identification are discussed. A summary of the most important MULTI-EDIP services with a focus on expert advice is described. An example of using the package in electroacoustic plant identification for active noise control system development is presented.
Rocznik
Strony
427--446
Opis fizyczny
Bibliogr. 43 poz., rys., tab.
Twórcy
autor
  • Institute of Automatic Control, The Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
autor
  • Institute of Automatic Control, The Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Bibliografia
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  • [10] J. Figwer and A. Niederliński: Using the DFT to synthesize multivariate orthogonal white noise series. Trans. of the Society for Computer Simulation, 12(2), (1995), 749-758.
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  • [17] J.Kasprzyk: Model structure determination in parametric model identification.Systems Science, 23(2), (1997), 89-95.
  • [18] J. Kasprzyk: MULTI-EDIP - an interactive software package for process identification.Proc. of the 13th IFAC Symp. on System Identification SYSID’2003, Rotterdam, Netherlands, (2003), 1484-1489.
  • [19] J. Kasprzyk: Model identification for active noise control - a case study. Archives of Control Sciences, 14(3), (2004), 219-243.
  • [20] J. Kasprzyk: Computer-Aided Process Identification. Silesian University of Technology Press, 1720, Gliwice, Poland, 2006, ISSN 0434-0760, (in Polish).
  • [21] S. M. Kay: Modern Spectral Estimation: Theory and Applications. Prentice Hall, Englewood Cliffs, 1986.
  • [22] I. Kollar, R. Pintelon and J. Schoukens: Frequency domain system identification toolbox for MATLAB: Characterizing nonlinear errors of linear models.Proc. of the IFAC Symp. on System Identification, SYSID’2006, Newcastle, Australia, Pergamon Press, (2006), 726-731.
  • [23] L. Ljung: System identification: Theory for the User. (Second ed.), Prentice-Hall, Upper Saddle River, New Jersey 1999.
  • [24] L. Ljung: System Identification Toolbox. User’s Guide. R2011a. The MathWorks Inc., 2011.
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  • [26] S. L. Marple: Digital Spectral Analysis with Applications. Prentice Hall, Englewood Cliffs, 1987.
  • [27] M. I. Michalczyk: Adaptive Control Algorithms for Three-Dimensional Zones of Quiet. Jacek Skalmierski Computer Studio, Gliwice, Poland, ISBN 83-89105-67-5, 2004.
  • [28] P. A. Nelson, S. J. Eliot: Active Control of Sound. Academic Press, London, 1992.
  • [29] NI LabVIEW System Identification Toolkit. (http://sine.ni.com/nips/cds/view/p/lang/en/nid/209045)
  • [30] A. Niederliński, J. Kasprzyk and J. Figwer: EFPI - an integrated software environment for system and signal identification. Proc. of the 9th IFAC/IFORS Symp. on Identification and System Parameter Estimation, Budapest, 1 (1991), 567-572.
  • [31] A. Niederliński, J. Kasprzyk and J. Figwer: EFPI - Expert for Process Identification. Software and User’s Manual. World Scientific Publishing Co. Ltd., 1994.
  • [32] A. Niederliński, J. Kasprzyk and J. Figwer: MULTI-EDIP - analyzer of multivariable signals and systems. Silesian University of Technology Press, Gliwice, Poland, 1997, ISSN 0434-0825, (in Polish).
  • [33] B. Ninness, A. Wills and S. Gibson: The University of Newcastle identification toolbox. 16th IFAC World Congress, Prague, Czech Republic, (2005).
  • [34] P. Van Overschee, B. de Moor, S. Boyd, H. Aling and R. Kosut: A fully interactive identification module for Xmath (ISID). Prep. of 10th IFAC Symp. on System Identification, Copenhagen, Denmark, 4 (1994), 1-2.
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  • [36] U. S. Pillai and T. I. Shim: Spectrum Estimation and System Identification.Springer-Verlag, New York, 1993.
  • [37] R. Pintelon and J. Schoukens: System Identification. A Frequency Domain Approach. IEEE Press, New York, 2001.
  • [38] T. Söderström and P. Stoica: System Identification. Prentice Hall International, London, 1989.
  • [39] P. Stoica and R. L. Moses: Introduction to Spectral Analysis. Prentice Hall, 1987.
  • [40] Z. Topor, L. Johannsen, J. Kasprzyk and J. Remmers: Dynamic ventilatory response to CO2 in congestive heart failure patients with and without central sleep apnea. J. of Applied Physiology, 91 (2001), 408-416.
  • [41] G. Schwarz: Estimating the dimension of a model. The Annals of Statistics, 6 (1978), 461-464.
  • [42] H. Unbehauen and G. P. Rao: Identification of Continuous-Time Systems.North Holland Systems and Control Series, Amsterdam, 1987.
  • [43] Y. Zhu: Use of error criteria in identification for control. Proc. of the 12th IFAC Symp. on System Identification (SYSID’2000), Santa Barbara, CA, 1 (2000).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5d7072b8-af4f-4921-aab3-5be9a45424c6
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