Title :
On tensor-product model based representation of neural networks
Author :
Rövid, András ; Szeidl, László ; Várlaki, Péter
Author_Institution :
John von Neumann Fac. of Infomatics, Obuda Univ., Budapest, Hungary
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.
Keywords :
approximation theory; neural nets; singular value decomposition; tensors; approximation methods; complex parameter varying model; higher order singular value decomposition; neural network representation; tensor-product model; Analytical models; Approximation methods; Artificial neural networks; Mathematical model; Numerical models; Singular value decomposition; Tensile stress;
Conference_Titel :
Intelligent Engineering Systems (INES), 2011 15th IEEE International Conference on
Conference_Location :
Poprad
Print_ISBN :
978-1-4244-8954-1
DOI :
10.1109/INES.2011.5954721
