Small-signal Modeling of InP HBT based on PSO-ELM Neural Network

Abstract

Extreme Learning Machine (ELM) is a new learning algorithm for single-hidden layer feedforward neural network, which has been widely used in lots of fields. However, it still has the insufficiency of randomly determining the hidden layer threshold and output weight, which leads to ill-conditioned output. In order to avoid the risk of decreasing prediction accuracy caused by this possibility, the ELM is optimized using particle swarm algorithm. A Particle Swarm Optimization (PSO) enhanced ELM algorithm is proposed to accurately model the small-signal properties of InP Heterojunction Bipolar Transistors (HBTs). PSOELM algorithm solves the problem of unstable prediction data caused by random determination of input weights in ELM. Comparing the modeling effects of the PSO-ELM model and the ELM model under different bias conditions for a 1 μm×15 μm InP HBT, it is proved that the PSO-ELM algorithm has better consistency with the measured data.