HOSVD based data representation and LPV model complexity reduction

  • Authors:

  • András Rövid;Péter Várlaki;László Szeidl

  • Affiliations:

  • Óbuda University, John von Neumann Faculty of Informatics, Budapest, Hungary;Széchenyi István University, System Theory Laboratory, Györ, Hungary;Széchenyi István University, System Theory Laboratory, Györ, Hungary

  • Venue:
  • AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
  • Year:
  • 2011

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Abstract

In the framework of the paper a HOSVD-based approach is introduced for LPV model reduction and multidimensional data representation. By these approaches in case of model reduction the system matrix is expressed with the help of a so called core tensor and a system of specially determined polylinear orthonormal functions. Similar approach will be applied also in case of data representation. The paper gives a detailed description on how to determine the polylinear functions and the core tensor and how the model can be expressed and reduced with their help. Furthermore, the HOSVD-based domain proposed for data representation will be compared to the well known frequency domain - related to Fourier transformation - from the point of view of their common application possibilities and their effectiveness.