This paper presents a methodology for identifying Reduced Vector Preisach Model parameters by using neural networks. The neural network used is a multiplayer perceptron trained with the Levenberg-Marquadt training algorithm. The network is trained by some hysteresis data, which are generated by using Reduced Vector Preisach Model with pre-assigned parameters. It is shown how a properly trained network is able to find the parameters needed to best fit a magnetization hysteresis curve.
TRAPANESE M (2008). Identification of parameters of dynamic Preisach model by neural networks. JOURNAL OF APPLIED PHYSICS, 103, 07D929-1-07D929-3 [10.1063/1.2836736].
Identification of parameters of dynamic Preisach model by neural networks
TRAPANESE, Marco
2008-01-01
Abstract
This paper presents a methodology for identifying Reduced Vector Preisach Model parameters by using neural networks. The neural network used is a multiplayer perceptron trained with the Levenberg-Marquadt training algorithm. The network is trained by some hysteresis data, which are generated by using Reduced Vector Preisach Model with pre-assigned parameters. It is shown how a properly trained network is able to find the parameters needed to best fit a magnetization hysteresis curve.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.