TY - GEN
T1 - Temperature Dependent I-V Models for Microwave Transistor Using Radial Basis NNs, Generalized Regression NNs and Feedforward NN
AU - Husain, Saddam
AU - Khan, Kashif
AU - Jarndal, Anwar
AU - Nauryzbayev, Galymzhan
AU - Hashmi, Mohammad
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by the Collaborative Research Grant (CRP) Number 021220CRP0222 at Nazarbayev University.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper carefully examines, evaluates and compare Radial Basis Neural Networks (RBNNs), Generalized Regression Neural Networks (GRNNs) and Feedforward Neural Network (FFNN) to devise an efficient, an expeditious and an accurate IV modelling scheme for Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT). The modelling schemes are employed on pulsed IV measurements of GaN HEMT device. Firstly, two variants of RBNNs namely, Exact Design (ED) and More Efficient Design (MED) networks are erected to simulate the temperature and bias dependence on the drain current. Thereafter, GRNNs and FFNN based models are developed. The hyperparameters associated with all the investigated models are tuned to improve the generalization capabilities of the models. Post tuning, the models are exploited to compute the mean squared error, mean absolute error and coefficient of determination to assess the models' performance. Lastly, the models are compared on the grounds of training and simulation time, parameters tuning time, generalization capability, computational efficiency and models' simplicity.
AB - This paper carefully examines, evaluates and compare Radial Basis Neural Networks (RBNNs), Generalized Regression Neural Networks (GRNNs) and Feedforward Neural Network (FFNN) to devise an efficient, an expeditious and an accurate IV modelling scheme for Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT). The modelling schemes are employed on pulsed IV measurements of GaN HEMT device. Firstly, two variants of RBNNs namely, Exact Design (ED) and More Efficient Design (MED) networks are erected to simulate the temperature and bias dependence on the drain current. Thereafter, GRNNs and FFNN based models are developed. The hyperparameters associated with all the investigated models are tuned to improve the generalization capabilities of the models. Post tuning, the models are exploited to compute the mean squared error, mean absolute error and coefficient of determination to assess the models' performance. Lastly, the models are compared on the grounds of training and simulation time, parameters tuning time, generalization capability, computational efficiency and models' simplicity.
KW - ANN
KW - GaN HEMTs and IV Modelling
KW - Generalized Regression Neural Networks
KW - Radial Basis Neural Networks
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U2 - 10.1109/IMPACT55510.2022.10029074
DO - 10.1109/IMPACT55510.2022.10029074
M3 - Conference contribution
AN - SCOPUS:85148029145
T3 - 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies, IMPACT 2022
BT - 2022 5th International Conference on Multimedia, Signal Processing and Communication Technologies, IMPACT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Multimedia, Signal Processing and Communication Technologies, IMPACT 2022
Y2 - 26 November 2022 through 27 November 2022
ER -