Fast Antenna Design Using Multi-Objective Evolutionary Algorithms and Artificial Neural Networks

摘要：

　　Aiming at reducing the large computation cost of traditional EM-driven antenna design methods,surrogate models based on back propagation neural networks(BPNN)are studied.In order to solve the problem of easily falling into local optimum in BPNN,a PSO-BPNN surrogate model is developed by improving initial structural parameters of neural networks and applied to fast multi-objective optimization design of multi-parameter antenna structures.Design results show that the proposed PSO-BPNN surrogate model can be integrating into multi-objective evolutionary algorithms for dealing with complex antenna designs with high-dimensional parameter space.

关键词： antenna design multi-objective evolutionary algorithms neural networks back propagation particle swarm optimization

作者: Wenwen Qin Jian Dong Meng Wang Yingjuan Li Shan Wang

作者单位: School of Information Science and Engineering Central South University Changsha 410083,China

会议类型: 国际会议

会议名称: The 12th International Symposium Antennas, Propagation, and EM Theory(ISAPE 2018)?(第十二届天线、传播与电磁理论国际学术会议)

会议地点: 杭州

会议语种:英文

页码: 1-3

在线出版日期: 2018-12-03（万方平台首次上网日期，不代表论文的发表时间）