Title :
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
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);
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
DOI :
10.1109/IITSI.2010.170
