Reliable Neural Modeling of pHEMT from a Smaller Number of Measurement Data

摘要

A systematic approach is presented to achieve a reliable neural model for microwave active devices with different numbers of training data. The method is implemented for a small-signal bias depended modeling of pHEMT in tow different environments, on a standard test-fixture and in the New Generation Quasi-Monolithic Integration Technology (NGQMIT), with different numbers of training data. The errors for different numbers of training data have been compared to each other and show that by using this method a reliable model is achievable even though the number of training data is considerably small. The method aims at constructing a model, which can satisfy the criteria of minimum training error, maximum smoothness (to avoid the problem of over-fitting), and simplest network structure.