Antenna Modeling Based on Image-CNN-LSTM

In response to the time-consuming and computationally intensive issues associated with using full-wave electromagnetic simulation software exploiting the global optimization method for antenna performance analysis, an efficient deep learning-based strategy is proposed and applied to the rapid modeling of antennas. Considering the outstanding performance of the convolutional neural network (CNN) in pattern recognition and the efficiency of recurrent neural network (RNN) with long short-term memory (LSTM) structures in processing sequential data, a hybrid CNN-LSTM network model is formed by combining CNN and LSTM structures. Furthermore, to enhance the model's performance, we apply the image model concept to the CNN-LSTM network, proposing an Image-CNN-LSTM hybrid network. The experimental result demonstrates that the proposed network shows significant advantages in terms of prediction accuracy and fitting effects, achieving an improvement in accuracy of about 40% and a coefficient of determination of about 4.5% compared to the CNN-LSTM network.