Temperature Characteristics Modeling for GaN PA Based on PSO-ELM

Area of Science:

• Electrical Engineering
• Materials Science
• Computational Intelligence

Background:

• Power amplifiers (PAs) are critical components in electronic systems.
• Accurate performance prediction and design optimization of PAs are essential.
• Gallium Nitride high-electron-mobility transistors (GaN HEMTs) offer high efficiency and power.

Purpose of the Study:

• To model the performance parameters of GaN HEMT PAs at varying temperatures.
• To compare the predictive capabilities of Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM) and Extreme Learning Machine (ELM).
• To provide theoretical support for optimizing PA design.

Main Methods:

• Modeling GaN HEMT PA performance using PSO-ELM and ELM algorithms.
• Evaluating model accuracy based on Mean Square Error (MSE).
• Analyzing the generalization ability of the models in handling temperature-dependent nonlinearities.

Main Results:

• The PSO-ELM model demonstrated superior prediction accuracy compared to the ELM model.
• A minimum MSE of 0.0006 was achieved with the PSO-ELM model.
• PSO-ELM exhibited a stronger generalization ability for temperature-related PA performance.

Conclusions:

• PSO-ELM is an effective method for modeling GaN HEMT PA performance across different temperatures.
• The enhanced generalization ability of PSO-ELM supports accurate performance prediction.
• This research offers valuable theoretical insights for optimizing PA design and performance.