Advanced Bayesian-Inspired Multilayer Effective Parameter Determination Method for Automated ANN Model Generation of Microwave Components

Artificial neural networks (ANNs) have revolutionized microwave computer-aided design by leveraging machine-learning techniques to tackle complex problems. One critical aspect of this process is the automatic modeling of the neural network structure. The conventional approach, which involves qualitative adjustments to prevent underfitting and overfitting, often leads to inefficiencies when the initial structure significantly deviates from the optimal one. This article presents a groundbreaking approach to address this issue, proposing an automatic modeling algorithm for neural networks. This algorithm employs Bayesian theory to optimize the ANN model structure. Based on the Bayesian theory, the formula for calculating effective parameters in a multihidden layer neural network is derived, allowing the initial structure to adopt any form and permitting efficient, quantitative adjustments. This innovative approach enables the computation of effective parameters under any given structure. A higher maximum number of effective parameters in multihidden layer ANN has been obtained compared to single-hidden layer ANN, thus improving modeling accuracy. Compared with the existing Bayesian-based automated ANN model generation methods, the proposed approach significantly enhances both modeling accuracy and speed. The effectiveness of this method is verified through the application of three microwave components.