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On tensor-product model based representation of neural networks

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3 Author(s)
Rovidz, A. ; John von Neumann Fac. of Infomatics, Obuda Univ., Budapest, Hungary ; Szeidl, L. ; Varlaki, P.

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.

Published in:

Intelligent Engineering Systems (INES), 2011 15th IEEE International Conference on

Date of Conference:

23-25 June 2011