Artificial Neural Network Method for the Analysis of 1-D Defected Ground Structure

Author

Wang, Shan ; Jiang, Yang ; Gao, Minghua ; Wang, Chuanyun ; Liu, Haiwen

Author_Institution

Sch. of Inf. Eng., East China Jiao Tong Univ., Nanchang, China

fYear

2010

fDate

2-4 April 2010

Firstpage

786

Lastpage

788

Abstract

A radial basis function neural network (RBFNN) is developed and applied to analyze one-dimension periodic defected ground structure (1-D DGS). This RBFNN is designed by Matlab program and used to the modeling and simulation of 1-D DGS circuits. The trained artificial neural network (ANN) model maps a set of scatting parameters of 1-D DGS in terms of its geometric parameters. A good agreement among ANN results, EM simulations and measurements verifies the validity of this proposed RBFNN model.

Keywords

electronic engineering computing; mathematics computing; microwave circuits; periodic structures; radial basis function networks; 1D periodic defected ground structure circuit; EM simulations; Matlab program; artificial neural network method; geometric parameters; radial basis function neural network; scatting parameters; Artificial neural networks; Circuit simulation; Computational modeling; Information analysis; Insertion loss; Mathematical model; Periodic structures; Propagation losses; Radial basis function networks; Solid modeling; artificial neural network (ANN); defected ground structure (DGS); modeling; radial basis function (RBF);

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Information Technology and Security Informatics (IITSI), 2010 Third International Symposium on

Conference_Location

Jinggangshan

Print_ISBN

978-1-4244-6730-3

Electronic_ISBN

978-1-4244-6743-3

Type

conf

DOI

10.1109/IITSI.2010.170

Filename

5453739