A neural network-differential evolution model for small signal recognition of PHEMTs

Since neural network algorithms are able to recognize nonlinear relations between different data sets, a neural network model (NN) based on a generalized differential evolution training algorithm (NN-DE) is presented for pseudomorphic high electron mobility transistor (PHEMT). The global optimization algorithm is applied to avoid the local minima problem in the gradient descent-training algorithm and to achieve acceptable solution. The main advantage of this technique is its validation in wide range of frequencies and high accuracy for the small signal characteristics. The proposed (NN-DE) model is used to predict the scattering parameter values for various bias values different from the ones in the data set used for training. This model has been verified by comparing predicted and measured values of a PHEMT for a certain data set of S-parameters at different frequencies and bias points.