Effective machine learning methods are usually trained by large data sets to guarantee accuracy and avoid overfitting. However, large data sets restrict the popularization of the machine learning methods when the data acquisition is nontrivial. To solve this problem, a novel machine learning method is proposed in this article based on the modified K-nearest neighbor (KNN) algorithm, which can extract more features from the data sets through the advanced workflow and simulation techniques. In the applications presented here, our method is 5–30 times faster than the traditional machine learning methods such as the artificial neural network (ANN) and Bayesian optimization by reducing the required size of the data sets. The proposed method is then employed to optimize the antenna parameters, while an additional branch is built to run the simulation tools (e.g., HFSS) and update the data set during the training process instead of constructing the data set beforehand. The validity and efficiency of this proposed method are confirmed by four different antenna examples and other machine learning and gradient-based algorithms. In summary, the proposed method can obtain a satisfactory optimal antenna design at little cost.

中文翻译：

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一种改进的高效 KNN 天线优化设计方法

有效的机器学习方法通​​常通过大数据集进行训练，以保证准确性并避免过度拟合。然而，当数据采集不平凡时，大数据集限制了机器学习方法的普及。为了解决这个问题，本文提出了一种基于改进的 K 近邻 (KNN) 算法的新型机器学习方法，该方法可以通过先进的工作流程和仿真技术从数据集中提取更多特征。在此处介绍的应用中，我们的方法通过减少所需的数据集大小，比人工神经网络 (ANN) 和贝叶斯优化等传统机器学习方法快 5-30 倍。然后采用所提出的方法来优化天线参数，同时构建了一个额外的分支来运行模拟工具（例如，HFSS）并在训练过程中更新数据集，而不是事先构建数据集。四种不同的天线示例以及其他机器学习和基于梯度的算法证实了该方法的有效性和效率。总之，所提出的方法可以以较小的成本获得令人满意的最佳天线设计。