Open AccessResearch on graphene/silicon pressure sensor array based on backpropagation neural network
Author(s) -
Li Fangqing,
Fang Shuxing,
Shen Yu,
Wang Debo
Publication year - 2021
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/ell2.12104
Subject(s) - backpropagation , artificial neural network , pressure sensor , artificial intelligence , graphene , rprop , sensor array , computer science , workflow , pattern recognition (psychology) , materials science , engineering , time delay neural network , machine learning , nanotechnology , types of artificial neural networks , mechanical engineering , database
In order to improve the recognition accuracy of graphene pressure sensors, a graphene/silicon pressure sensor array is studied based on backpropagation (BP) neural network. The principle of the pressure sensor array and the workflow of BP neural network are introduced. The 100 groups of training samples ranging from 0 to 1000 kPa are studied based on levenberg‐marquardt (L‐M) optimisation algorithm, and a multiple BP (M‐BP) neural network is designed to improve the recognition accuracy of the pressure sensor array. The training recognition accuracy and test recognition accuracy of M‐BP neural network are about 99.9%. This work plays an important role in the application of graphene pressure sensors in more fields, especially in the solutions for weak pressure detection in artificial intelligence and human‐computer interaction.