Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

随机森林 支持向量机 机器学习 计算机科学 人工智能 高电子迁移率晶体管 超参数 人工神经网络 决策树 非线性系统 Boosting(机器学习) 阿达布思 梯度升压 工程类 晶体管 量子力学 电气工程 物理 电压
作者
Bo Liu,Jialin Cai
出处
期刊:International Journal of Numerical Modelling-electronic Networks Devices and Fields [Wiley]
卷期号:37 (2) 被引量:2
标识
DOI:10.1002/jnm.3177
摘要

Abstract A variety of novel behavioral modeling techniques have been reported to accurately capture the nonlinear characteristics of GaN devices. For the purpose of describing GaN HEMT large‐signal behavior, machine learning‐based approaches have been proposed. There is, however, a lack of comparison and analysis of their performance with different machine learning techniques in these studies. An examination of machine learning‐based large‐signal modeling techniques is presented in this article. To develop large‐signal models of GaN HEMT, a range of commonly used modeling techniques such as artificial neural networks (ANN), random sample consistency (RANSAC), support vector regression (SVR), Gaussian process regression (GPR), decision trees (DTs), and genetic algorithm‐assisted ANN (GA‐ANN) are described and employed. Afterwards, integrated modeling techniques such as Bootstrap aggregation (BA), random forests (RF), AdaBoost, and gradient tree boosting (GTB) are reviewed and tested for their capabilities in developing GaN HEMT LSM. The hyperparameters of each built model are modified using the random search optimization (RSO) method, and five‐fold cross‐validation is used during validation. In order to identify optimal algorithms for nonlinear behavioral modeling of GaN devices, the model prediction results are comprehensively analyzed.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
liuyf发布了新的文献求助10
刚刚
刚刚
zhangjiegxf发布了新的文献求助10
1秒前
1秒前
Cheng完成签到 ,获得积分0
1秒前
隐形曼青应助晨屿采纳,获得10
1秒前
李玲玲完成签到,获得积分10
1秒前
CipherSage应助00采纳,获得10
3秒前
3秒前
3秒前
wanci应助YY采纳,获得10
4秒前
4秒前
4秒前
gwff发布了新的文献求助10
5秒前
5秒前
6秒前
留猪完成签到,获得积分10
6秒前
Cici发布了新的文献求助10
6秒前
周常通发布了新的文献求助10
6秒前
潇洒的布偶完成签到,获得积分10
6秒前
仁爱绝义发布了新的文献求助10
6秒前
汉堡包应助贵贵采纳,获得10
6秒前
7秒前
稳重安露发布了新的文献求助30
7秒前
7秒前
8秒前
在水一方应助平家boy采纳,获得10
8秒前
专一的吐司完成签到,获得积分10
9秒前
9秒前
量子星尘发布了新的文献求助30
10秒前
10秒前
10秒前
sensen发布了新的文献求助10
10秒前
上官若男应助Young采纳,获得10
10秒前
10秒前
慕青应助坚果采纳,获得10
11秒前
zhangjiegxf完成签到,获得积分10
11秒前
holland完成签到 ,获得积分10
11秒前
wwww发布了新的文献求助10
11秒前
ycd关闭了ycd文献求助
12秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 360
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3971277
求助须知:如何正确求助?哪些是违规求助? 3515939
关于积分的说明 11180280
捐赠科研通 3251061
什么是DOI,文献DOI怎么找? 1795664
邀请新用户注册赠送积分活动 875937
科研通“疑难数据库(出版商)”最低求助积分说明 805209