Temperature Characteristics Modeling for GaN PA Based on PSO-ELM
Qian Lin, Meiqian Wang
TL;DR
This paper uses a PSO-ELM model to predict and optimize the performance of GaN power amplifiers at different temperatures, showing better accuracy than traditional methods.
Contribution
The novel use of PSO-ELM for temperature-based performance modeling of GaN PAs, achieving higher prediction accuracy.
Findings
The PSO-ELM model achieves a minimum MSE of 0.0006, outperforming the ELM model.
PSO-ELM demonstrates stronger generalization in capturing nonlinear temperature-performance relationships.
Abstract
In order to solve the performance prediction and design optimization of power amplifiers (PAs), the performance parameters of Gallium Nitride high-electron-mobility transistor (GaN HEMT) PAs at different temperatures are modeled based on the particle swarm optimization–extreme learning machine (PSO-ELM) and extreme learning machine (ELM) in this paper. Then, it can be seen that the prediction accuracy of the PSO-ELM model is superior to that of ELM with a minimum mean square error (MSE) of 0.0006, which indicates the PSO-ELM model has a stronger generalization ability when dealing with the nonlinear relationship between temperature and PA performance. Therefore, this investigation can provide vital theoretical support for the performance optimization of PA design.
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Taxonomy
TopicsMachine Learning and ELM · GaN-based semiconductor devices and materials · Advanced Memory and Neural Computing