Bayesian-Assisted Multilayer Neural Network Structure Adaptation Method for Microwave Design

Abstract

Automated model generation (AMG) is a systematic and efficient method to develop an artificial neural network (ANN) model for microwave components. By incorporating the Bayesian theory into AMG, the most compact multilayer perceptron (MLP) model with the highest accuracy can be developed. However, the existing Bayesian-based AMG method is only suitable for single-hidden-layer MLP. In this letter, we propose a novel Bayesian-assisted AMG method for neural networks with multiple hidden layers. A systematic algorithm is proposed to determine the optimal number of hidden layers and the number of hidden neurons in each hidden layer. Using the proposed method, a compact and accurate MLP model with a multi-hidden layer structure can be systematically developed. The proposed method can achieve a higher model accuracy than the previous Bayesian-based method with almost the same number of model network parameters. Two microwave filters are used to demonstrate the advantages of the proposed method.