DIRECT SOLUTION METHOD FOR FINITE-ELEMENT ANALYSIS USING HOPFIELD NEURAL-NETWORK

被引：15

作者：

YAMASHITA, H

KOWATA, N

CINGOSKI, V

KANEDA, K

机构：

[1] Hiroshima Univ, Higashi-hiroshima, Japan

来源：

IEEE TRANSACTIONS ON MAGNETICS | 1995年 / 31卷 / 03期

关键词：

Electromagnetic field theory;

D O I：

10.1109/20.376426

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：

One property of the Hopfield neural network is the monotonous minimization of energy as time proceeds. In this paper, this property is applied to minimize the energy functional obtained by ordinary finite element analysis. The mathematical representation and correlation between finite element and neural network calculus are presented. The selection of the sigmoid function and its influence on the iteration process is discussed. The obtained results using the proposed method show excellent agreement with theoretical solutions.

引用

页码：1964 / 1967

页数：4

共 6 条

[1]

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[3]

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[4]

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[5]

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[6]

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