Large-Signal Behavior Modeling of GaN P-HEMT Based on GA-ELM Neural Network

• Published in: Circuits, systems, and signal processing Vol. 41; no. 4; pp. 1834 - 1847
• Main Authors: Wang, Shaowei, Zhang, Jincan, Liu, Min, Liu, Bo, Wang, Jinchan, Yang, Shi
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2022; Springer Nature B.V
• Subjects: Artificial neural networks; Back propagation; Back propagation networks; Circuits and Systems; Electrical Engineering; Electronics and Microelectronics; Engineering; Gallium nitrides; Genetic algorithms; Hierarchies; High electron mobility transistors; Instrumentation; Machine learning; Modelling; Neural networks; Signal,Image and Speech Processing; GA-ELM neural network algorithm; GaN large-signal model; GA-BP neural network algorithm; BP neural network algorithm
• ISSN: 0278-081X; 1531-5878
• DOI: 10.1007/s00034-021-01891-7

The Genetic Algorithm-Extreme Learning Machine (GA-ELM) neural network algorithm is proposed to model the relevant characteristics of GaN pseudomorphic high electron mobility transistor (P-HEMT) large signal. This algorithm solves the over-fitting problem of the Back Propagation (BP) neural network algorithm in the prediction data. It has the characteristics of fast calculation speed, so it can greatly save calculation processing time. It can also randomly generate the connection weights of the input layer, the hidden layer and the threshold of the hidden layer neurons, avoiding errors in parameter selection. In order to verify the superiority of the algorithm, the modeling effects of the BP neural network algorithm model, the Genetic Algorithm-Back Propagation (GA-BP) neural network algorithm model and the GA-ELM neural network algorithm model are compared in this paper. The results show that the proposed GA-ELM neural network algorithm model has the highest accuracy.