On tensor-product model based representation of neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

The approximation methods of mathematics are widely used in theory and practice for several problems. In the framework of the paper a novel tensor-product based approach for representation of neural networks (NNs) is proposed. The NNs in this case stand for local models based on which a more complex parameter varying model can numerically be reconstructed and reduced using the higher order singular value decomposition (HOSVD). The HOSVD as well as the tensor-product based representation of NNs will be discussed in detail.

Original languageEnglish
Title of host publicationINES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings
Pages69-72
Number of pages4
DOIs
Publication statusPublished - Aug 22 2011
Event15th International Conference on Intelligent Engineering Systems, INES 2011 - Poprad, Slovakia
Duration: Jun 23 2011Jun 25 2011

Publication series

NameINES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings

Other

Other15th International Conference on Intelligent Engineering Systems, INES 2011
CountrySlovakia
CityPoprad
Period6/23/116/25/11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Rövid, A., Szeidl, L., & Várlaki, P. (2011). On tensor-product model based representation of neural networks. In INES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings (pp. 69-72). [5954721] (INES 2011 - 15th International Conference on Intelligent Engineering Systems, Proceedings). https://doi.org/10.1109/INES.2011.5954721