| Autor: |
Qian Lin, Meiqian Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Micromachines, Vol 15, Iss 8, p 1008 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2072-666X |
| DOI: |
10.3390/mi15081008 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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