A Modified Efficient KNN Method for Antenna Optimization and Design
Liangze Cui, Yao Zhang, Runren Zhang, Qing Liu
Abstract
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.