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.
2008
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].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/24800
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