TY - GEN
T1 - On Temperature-Dependent Small-Signal Behavioral Modelling of GaN HEMT Using GWO-PSO and WOA
AU - Khan, Kashif
AU - Husain, Saddam
AU - Nauryzbayev, Galymzhan
AU - Hashmi, Mohammad
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The accuracy and convergence of Artificial Neural Network (ANN) based models are contingent on initial weights as they employ backpropagation algorithm. To address these issues, this paper investigates and develops accurate Global Optimization (GO) assisted ANN based small-signal behavioral models for Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT). Two potent explorer-exploiter frameworks-based optimization algorithms namely Grey Wolf Optimization-Particle Swarm Optimization (GWO-PSO) and Whale Optimization Algorithm (WOA) are utilized to fine-tune the initial weights of ANN. Thereafter, ANN, GWO-PSO- and WOA-assisted ANN based behavioral models are thoroughly examined and evaluated for various regression metrics. A strong correlation between the predicted and measured scattering parameters across the entire frequency spectrum is observed. We found both GO assisted ANN based models produced accurate models. However, GWO-PSO-ANN based models have shown better convergence behavior and at the same time the most accurate among all the tested models in this paper.
AB - The accuracy and convergence of Artificial Neural Network (ANN) based models are contingent on initial weights as they employ backpropagation algorithm. To address these issues, this paper investigates and develops accurate Global Optimization (GO) assisted ANN based small-signal behavioral models for Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT). Two potent explorer-exploiter frameworks-based optimization algorithms namely Grey Wolf Optimization-Particle Swarm Optimization (GWO-PSO) and Whale Optimization Algorithm (WOA) are utilized to fine-tune the initial weights of ANN. Thereafter, ANN, GWO-PSO- and WOA-assisted ANN based behavioral models are thoroughly examined and evaluated for various regression metrics. A strong correlation between the predicted and measured scattering parameters across the entire frequency spectrum is observed. We found both GO assisted ANN based models produced accurate models. However, GWO-PSO-ANN based models have shown better convergence behavior and at the same time the most accurate among all the tested models in this paper.
KW - ANN
KW - GaN HEMTs
KW - GWO-PSO
KW - WOA
UR - http://www.scopus.com/inward/record.url?scp=85179847802&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179847802&partnerID=8YFLogxK
U2 - 10.1109/ISNCC58260.2023.10323697
DO - 10.1109/ISNCC58260.2023.10323697
M3 - Conference contribution
AN - SCOPUS:85179847802
T3 - 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023
BT - 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Symposium on Networks, Computers and Communications, ISNCC 2023
Y2 - 23 October 2023 through 26 October 2023
ER -