Title :
Neural-Network Based Modelling Approach for Loading Systems
Author :
Rövid, András ; Varlaki, Peter ; Orban, Gabriella ; Vadvari, Tibor
Author_Institution :
John von Neumann Fac. of Infomatics, Obuda Univ., Budapest, Hungary
Abstract :
As in many fields in modern logistics the system modelling and identification play an important role. By the modelling of complex non-linear systems different model approximation approaches are utilized. The approximation methods of mathematics are widely used in theory and practice for several problems. In the framework of the paper a higher order singular value decomposition (HOSVD) based approximation approach for neural network (NN) model approximation is introduced. The approach will be detailed from the point of view of logistic systems but it may be applicable for other fields, as well. 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 HOSVD.
Keywords :
approximation theory; loading; logistics; neural nets; nonlinear control systems; singular value decomposition; HOSVD; approximation method; complex nonlinear system; complex parameter varying model; higher order singular value decomposition based approximation approach; loading system; neural-network based modelling approach; system identification; system modelling; Artificial neural networks; Load modeling; Loading; Numerical models; Tensile stress; Vehicles; loading systems; modelling; neural network; system identification; tensor-product;
Conference_Titel :
Emerging Trends in Engineering and Technology (ICETET), 2011 4th International Conference on
Conference_Location :
Port Louis
Print_ISBN :
978-1-4577-1847-2
DOI :
10.1109/ICETET.2011.69