A Modular Neural Network for Global Modeling of Microwave Transistors

In this paper we present a modular neural network structure for global modeling of microwave transistors (MESFET/HEMT). The model is able to accurately represent both, the small-signal and the large-signal behavior of the device. This is achieved by means of an original neural architecture, which is composed of two main modules. The first module captures the nonlinear dynamic I/V characteristic of the transistor, which governs the large signal behavior of the device. The second module estimates the derivatives at the operation (bias) point by means of a neural network and then it locally reconstructs the function by means of a third order Taylor series around that point. This second module is able to reproduce the small-signal intermodulation behavior. These two modules are combined into a global model by means of a simple fuzzy controller. In this way the global model represents adequately the device behavior independently of the nature of the applied signals. I. Introduction The d..