### Abstract

Lingua originale | English |
---|---|

pagine (da-a) | 07D929-1-07D929-3 |

Rivista | Journal of Applied Physics |

Volume | 103 |

Stato di pubblicazione | Published - 2008 |

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### All Science Journal Classification (ASJC) codes

- Physics and Astronomy(all)

### Cita questo

*Journal of Applied Physics*,

*103*, 07D929-1-07D929-3.

**Identification of parameters of dynamic Preisach model by neural networks.** / Trapanese M.

Risultato della ricerca: Article

*Journal of Applied Physics*, vol. 103, pagg. 07D929-1-07D929-3.

}

TY - JOUR

T1 - Identification of parameters of dynamic Preisach model by neural networks

AU - Trapanese M

AU - Trapanese, Marco

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10447/24800

M3 - Article

VL - 103

SP - 07D929-1-07D929-3

JO - Journal of Applied Physics

JF - Journal of Applied Physics

SN - 0021-8979

ER -