摘要
为了加快天线建模速度,针对现有天线设计软件天线参数优化速度过慢问题进行了建模研究。首先通过几种常用的启发式算法优化后的多层前馈(Back propagation,BP)神经网络对天线参数进行优化比较,并对其中最优的算法遗传算法优化BP(Genetic algorithm BP,GABP)神经网络算法进行深度改进。其次采用自适应算法和模拟退火算法优化算法对GABP进行优化。最终通过模拟试验验证出自适应GABP算法对于天线参数优化的误差最小。该研究为天线设计软件中天线优化方法提供了一种误差较小的新方法,拥有更高的预测准确度,拟合速度也大大提升。实验对比证明了该算法的可行性。
To speed up the antenna modeling and optimization,this paper conducts a modeling study for antenna parameter optimization by the commercially available antenna design software.Firstly,the back propagation(BP)neural networks are optimized by several commonly‑used heuristic algorithms,and used to improve the antenna parameters.These parameters are compared and the best one is the one optimized by genetic algorithm BP(GABP).Secondly,the adaptive algorithm and simulated annealing algorithm is used to optimize GABP.Finally,the minimum error of the adaptive GABP algorithm for antenna parameter optimization is verified by simulation tests.The study provides a new method for antenna optimization in antenna design software with less errors.It has higher prediction accuracy and much faster fitting speed.The feasibility of this algorithm is also demonstrated by experimental comparison.
作者
侯大成
张昊宇
林一帆
张万祥
HOU Dacheng;ZHANG Haoyu;LIN Yifan;ZHANG Wanxiang(School of Information Engineering,Zhejiang Ocean University,Zhoushan 316022,China;School of Marine Engineering Equipment,Zhejiang Ocean University,Zhoushan 316022,China)
出处
《数据采集与处理》
CSCD
北大核心
2023年第5期1172-1179,共8页
Journal of Data Acquisition and Processing
基金
省属高校基本科研项目(2021J016)。
关键词
启发式算法
模拟退火算法
自适应
天线阵列
heuristic algorithm
simulated annealing algorithm
self adaptive
antenna array
