Automated model generation for electronic devices using neural network approaches

Abstract

This paper describes artificial neural network approaches to convert the conventionally expensive process of electronic modelling into an automated model generation process. Artificial neural networks are trained using machine learning algorithm to learn the electronic behaviour, and the trained neural network becomes a model to help predict the electronic device behaviour. The automated model generation algorithm performs adaptive data sampling to determine the amount and the distribution of training data needed to train neural networks. The algorithm also determines the number of hidden neurons needed to achieve a compact and accurate model. Also incorporated into the automated model generation method is an efficient interpolation approach to make the process much faster. The objective of the described method is to generate a compact neural-network based model with better accuracy and in less time than conventional approach. Examples of automated modeling of radio-frequency and microwave filters used in wireless electronic systems are described showing the advantage of this technique.