A Review of Recent Neural Network Approaches to the Modeling of Nonlinear Microwave Devices

Abstract

Nonlinear microwave device modeling is an important part of computer-aided design (CAD) and many papers have been published in the literature. This paper presents a review of recent neural network approaches to the modeling of nonlinear microwave device including the dynamic Neuro-SM approach and the Wiener-type dynamic neural network approach and its applications. DC, small-signal and large-signal harmonic data are used as training data. The neural network based methods can fast and accurately build accurate models for nonlinear microwave devices. Compared with conventional equivalent circuit models, the models generated by these neural network based methods are more efficient to represent the behavior of the device.