Utilizing Feed Forward Neural Networks for acceleration of global optimization procedures

被引：17

作者：

Ebner, T ^{[1
]}

Magele, C ^{[1
]}

Brandstatter, BR ^{[1
]}

Richter, KR ^{[1
]}

机构：

[1] Graz Univ Technol, Inst Grundlagen & Theorie Elektrotech, A-8010 Graz, Austria

来源：

IEEE TRANSACTIONS ON MAGNETICS | 1998年 / 34卷 / 05期

关键词：

neural networks; generalized functions; approximation methods; optimization methods;

D O I：

10.1109/20.717683

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：

Global optimization in Electrical Engineering usually requires an enormous amount of CPU time to evaluate the objective function when stochastic methods are used. Approximating the objective function can drastically reduce the computational demands. The use of Feed Forward Neural Networks is proposed in this paper and its application is investigated using a unconstrained and a constrained version of the TEAM: Workshop problem 22.

引用

页码：2928 / 2931

页数：4

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