Convolutional Neural Network With Adaptive Batch-Size Training Technique for High-Dimensional Inverse Modeling of Microwave Filters

Abstract

This letter proposes a convolutional neural network (CNN) modeling technique with an adaptive batch-size training technique for high-dimensional inverse modeling of microwave filters. Real and imaginary parts of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> -parameters are used as two-channel model inputs and coupling matrix of the filter is used as the model output. Since smooth activation function is needed for microwave modeling, the sigmoid function is introduced as the activation function in the proposed CNN. To further reduce the training time and increase the modeling accuracy, we propose an adaptive batch-size training strategy for developing the proposed CNN model. The proposed CNN inverse model with the adaptive batch size training strategy is demonstrated using two high-dimensional microwave filter examples.