On efficient modeling of drain current for designing high-power GaN HEMT-based circuits
In this paper, different modeling approaches to the drain current, including analytical and
artificial neural network (ANN) modeling, are investigated. The adopted models address the …
artificial neural network (ANN) modeling, are investigated. The adopted models address the …
Evaluating Nine Machine Learning Algorithms for GaN HEMT Small Signal Behavioral Modeling through K-fold Cross-Validation
N Ahmad, V Nath - Engineering, Technology & Applied Science …, 2024 - etasr.com
This paper presents an investigation into the modeling of Gallium Nitride (GaN) High
Electron Mobility Transistors (HEMTs) using multiple Machine Learning (ML) algorithms …
Electron Mobility Transistors (HEMTs) using multiple Machine Learning (ML) algorithms …
GaN FET Model Validation Via Experimental Multiple Pulse Test
V Barba, S Musumeci, M Palma - 2024 Energy Conversion …, 2024 - ieeexplore.ieee.org
In power electronics design, validating the electronics switches' models is essential. The
most recent transistor in wide-bandgap technology is the Gallium-Nitride FET (GaN FET) …
most recent transistor in wide-bandgap technology is the Gallium-Nitride FET (GaN FET) …
Comparison of ANFIS and ANN for Small-Signal Modelling of GaN HEMT up to 40 GHz
This paper compares the performance of the Adaptive Neuro-Fuzzy Interface System
(ANFIS) and Artificial Neural Network (ANN) by developing small-signal models for Gallium …
(ANFIS) and Artificial Neural Network (ANN) by developing small-signal models for Gallium …
Development and Evaluation of ANN, RBNNs, and GRNNs Based Small-Signal Behavioral Models for GaN HEMT Up to 40 GHz
This paper conducts an extensive analysis of small-signal behavioral modelling of Gallium
Nitride (GaN) High Electron Mobility Transistors (HEMTs) up to 40 GHz, utilizing Artificial …
Nitride (GaN) High Electron Mobility Transistors (HEMTs) up to 40 GHz, utilizing Artificial …
PREDICTING STRESS CONCENTRATION ACTORS IN TENSIONLOADED SHAFTS USING ARTIFICIAL NEURAL NETWORKS
N Kostic, M Vesna, N Petrovic, S Milenković - 2024 - scidar.kg.ac.rs
This paper presents a novel approach to determining the stress concentration factor (Kt) for
tension-loaded machine parts using artificial neural networks (ANNs). Analytical methods for …
tension-loaded machine parts using artificial neural networks (ANNs). Analytical methods for …