Multiobjective Optimization of Antenna Inverse Design With Data Augmentation Based on K-Means-NN

IF 4.8 2区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Meng Wang;Shirui Yu;Jian Dong;Heng Luo;Chengwang Xiao
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引用次数: 0

Abstract

To enhance the accuracy of the inverse model with a limited number of antenna sample points, a data augmentation method based on K-means and forward neural networks (FNNs) is proposed. In this method, K-means is used to partition the initial imbalanced dataset, while a perturbation factor and FNNs are introduced to oversample the minority samples, ensuring a balanced performance distribution. A weighted loss combining soft dynamic time warping and mean squared error is embedded in FNNs to predict antenna performance more accurately. The convolutional inverse neural network integrates multiple performance features stacked along a specific dimension and is iteratively invoked until the optimized antenna meets the desired objectives. Taking a wideband circularly polarized antenna as a numerical example, the proposed method requires fewer electromagnetic simulations than other advanced optimization techniques.
基于K-Means-NN数据增强的天线逆设计多目标优化
为了在天线采样点数量有限的情况下提高反演模型的精度,提出了一种基于k均值和前向神经网络的数据增强方法。在该方法中,使用K-means对初始不平衡数据集进行划分,同时引入扰动因子和fnn对少数样本进行过采样,以确保平衡的性能分布。在fnn中嵌入软动态时间翘曲和均方误差相结合的加权损失,以更准确地预测天线性能。卷积反神经网络将沿特定维度叠加的多个性能特征集成在一起,并迭代调用,直到优化后的天线满足预期目标。以宽带圆极化天线为例,与其他先进的优化技术相比,该方法需要较少的电磁仿真。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.00
自引率
9.50%
发文量
529
审稿时长
1.0 months
期刊介绍: IEEE Antennas and Wireless Propagation Letters (AWP Letters) is devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation. These are areas of competence for the IEEE Antennas and Propagation Society (AP-S). AWPL aims to be one of the "fastest" journals among IEEE publications. This means that for papers that are eventually accepted, it is intended that an author may expect his or her paper to appear in IEEE Xplore, on average, around two months after submission.
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