A FAST SYSTEMATIC APPROACH FOR MICROSTRIP ANTENNA DESIGN AND OPTIMIZATION USING ANFIS AND GA

被引:22
作者
Dadgarnia, A. [1 ]
Heidari, A. A. [1 ,2 ]
机构
[1] Yazd Univ, Commun Res Lab, Yazd, Iran
[2] Yazd Univ, Dept Elect Engn, Yazd, Iran
关键词
PARTICLE SWARM OPTIMIZATION; BANDWIDTH;
D O I
10.1163/156939310793699037
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];
摘要
In this paper an approach for optimizing the physical parameters of microstrip antennas is presented. Although this method can be used for optimizing any kind of antennas, our focus is on ultra wide-band (UWB) microstrip antennas. Adaptive neuro-fuzzy inference systems (ANFIS) trained by hybrid learning algorithm are used for estimating the properties of the desired antennas. The data required for training these networks is extracted from HFSS. By introducing a proper objective function and applying genetic algorithm (GA) to it, the physical parameters of the antenna are determined for bandwidth maximization. In the optimization process, instead of running HFSS repetitively, trained ANFISs are used for antenna characteristics estimation, and therefore the optimization time in this method is much lower than other techniques. Comparison of the parameters obtained by our method and those reported previously shows the accuracy and efficiency of the method.
引用
收藏
页码:2207 / 2221
页数:15
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