Global Optimization of Neural Network-Based Electrothermal Model for GaN Transistors

被引：0

作者：

Jarndal, Anwar ^{[1
,2
]}

Hamdan, Sadeque ^{[1
]}

机构：

[1] Univ Sharjah, SEAM, Res Grp, POB 27272, Sharjah, U Arab Emirates

[2] Univ Sharjah, Elect & Comp Engn Dept, POB 27272, Sharjah, U Arab Emirates

来源：

2017 INTERNATIONAL CONFERENCE ON ELECTRICAL AND COMPUTING TECHNOLOGIES AND APPLICATIONS (ICECTA) | 2017年

关键词：

Transistor modeling; Neural networks; Genetic algorithm; Moth-flame optimization; Multi-verse optimization; HEMTS;

D O I：

暂无

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：

This paper presents an efficient artificial neural network (ANN) electrothermal modeling approach applied to GaN devices. Global optimization based learning procedures, including genetic algorithm (GA), Multi-Verse (MV) and Moth-Flame (MF) have been implemented and validated. The three modeling approaches show a very good fitting for actual measurement with competitive results for recent MV and MF techniques with respect to the widely used GA one.

引用

页码：523 / 526

页数：4

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