Robust neural based microwave modelling and design using advanced model extrapolation

Abstract

For the first time, the issue of using neural-based microwave models far outside their training range is directly addressed. A standard neural model is meaningful only outside the particular range of inputs for which it is trained, and becomes unreliable when used outside this range. This paper presents a robust neural modelling technique incorporating advanced extrapolation to address this problem. A new process is incorporated in training to formulate a set of base points to represent a regular or irregular training region. An adaptive base point selection method is developed to identify the most significant subset of base points upon any given value of model input. This method is combined with quadratic extrapolation utilizing neural network outputs and their derivatives. The proposed technique is demonstrated by examples of neural based design solution space analysis of coupled transmission lines and neural based behaviour modelling and simulation of power amplifiers. It is demonstrated that the proposed technique allows the neural based microwave models to be used far beyond their original training range.