Modeling of sic mesfets by using support vector machine regression

被引：11

作者：

Xu, Y. ^{[1
]}

Guo, Y. ^{[1
]}

Xu, R. ^{[1
]}

Wu, Y. ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Elect Engn, Chengdu 610054, Sichuan, Peoples R China

来源：

JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS | 2007年 / 21卷 / 11期

关键词：

D O I：

10.1163/156939307782000361

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：

In this paper, a support vector machine (SVM) regression approach is introduced for modeling of field effect transistors (FETs). Benefits to the good generalization ability of SVM, a SVM regression (SVR) model is established using a set of training and testing data, which is produced by simulation using an available empirical model of SiC MESFETs. Experimental results show the SVR model has good ability in predicting electrical performance of FETs.

引用

页码：1489 / 1498

页数：10

共 50 条

[1] AN SUPPORT VECTOR REGRESSION BASED NONLINEAR MODELING METHOD FOR SIC MESFET
Xu, Y.
Guo, Y.
Xu, R.
Xia, L.
Wu, Y.
[J]. PROGRESS IN ELECTROMAGNETICS RESEARCH LETTERS, 2008, 2 : 103 - 114
[2] Modeling of Proton Exchange Membrane Fuel Cell Using Support Vector Regression Machine
Tang, Jiangling
Huang, Jian
[J]. PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON INFORMATION ENGINEERING FOR MECHANICS AND MATERIALS, 2015, 21 : 380 - 385
[3] Modeling of 3-D vertical interconnect using support vector machine regression
Xia, Lei
Meng, Jicheng
Xu, Ruimin
Yan, Bo
Guo, Yunchuan
[J]. IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS, 2006, 16 (12) : 639 - 641
[4] Tide modelling using support vector machine regression
Okwuashi, Onuwa
Ndehedehe, Christopher
[J]. JOURNAL OF SPATIAL SCIENCE, 2017, 62 (01) : 29 - 46
[5] Interval regression analysis using support vector machine and quantile regression
Hwang, CH
Hong, DH
Na, E
Park, H
Shim, J
[J]. FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY, PT 1, PROCEEDINGS, 2005, 3613 : 100 - 109
[6] Liver fat analysis using optimized support vector machine with support vector regression
Pushpa, B.
Baskaran, B.
Vivekanandan, S.
Gokul, P.
[J]. TECHNOLOGY AND HEALTH CARE, 2023, 31 (03) : 867 - 886
[7] Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery
Raquel Rodríguez-Pérez
Jürgen Bajorath
[J]. Journal of Computer-Aided Molecular Design, 2022, 36 : 355 - 362
[8] Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery
Rodriguez-Perez, Raquel
Bajorath, Juergen
[J]. JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, 2022, 36 (05) : 355 - 362
[9] Exploring Alternative Strategies for the Identification of Potent Compounds Using Support Vector Machine and Regression Modeling
Miyao, Tomoyuki
Funatsu, Kimito
Bajorath, Juergen
[J]. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2019, 59 (03) : 983 - 992
[10] Regression depth and support vector machine
Christmann, Andreas
[J]. DATA DEPTH: ROBUST MULTIVARIATE ANALYSIS, COMPUTATIONAL GEOMETRY AND APPLICATIONS, 2006, 72 : 71 - 85

← 1 2 3 4 5 →