A new neural-network-based scalar hysteresis model

被引：26

作者：

Kuczmann, M ^{[1
]}

Iványi, A ^{[1
]}

机构：

[1] Budapest Univ Technol & Econ, Dept Electromagnet Theory, H-1521 Budapest, Hungary

来源：

IEEE TRANSACTIONS ON MAGNETICS | 2002年 / 38卷 / 02期

关键词：

feedforward-type neural networks; Preisach model; scalar hysteresis model;

D O I：

10.1109/20.996221

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A neural network (NN)-based model of scalar hysteresis characteristics has been developed for modeling the behavior of magnetic materials. The virgin curve and a set of the first-order reversal branches can be stored preliminary in a system of three NNs. Different properties of magnetic materials can be simulated by a simple if-then type knowledge-based algorithm. Hysteresis characteristics of different materials predicted by the introduced model are compared with the results of the classical Preisach simulation technique. Comparisons are plotted in figures.

引用

页码：857 / 860

页数：4

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