Information Management System of Student Laboratory Based on BP Neural Network

Yunfan Sun

2022

Abstract

The laboratory is an important part of cultivating students’ practical ability and experimental skills. With the development of education, the teaching mode of experimental courses has changed from traditional experimental teaching to today’s open experimental teaching. The purpose of this paper is to research student laboratory information management system based on BP neural network. The investigation and analysis of the student laboratory information management system based on BP neural network is carried out, and the key technologies involved in the construction of the system are discussed. Using the advantages of artificial neural network in data prediction, a three-layer feedforward network model based on BP algorithm is built, and a framework corresponding to this model is constructed in the system, and the predicted value of laboratory IGBT devices is verified by simulation results. The results show that the BP neural network can accurately predict the number of IGBT devices.

Paper Citation

in Harvard Style

Sun Y. (2022). Information Management System of Student Laboratory Based on BP Neural Network. In Proceedings of the 2nd International Conference on New Media Development and Modernized Education - Volume 1: NMDME; ISBN 978-989-758-630-9, SciTePress, pages 362-366. DOI: 10.5220/0011912200003613

in Bibtex Style

@conference{nmdme22,
author={Yunfan Sun},
title={Information Management System of Student Laboratory Based on BP Neural Network},
booktitle={Proceedings of the 2nd International Conference on New Media Development and Modernized Education - Volume 1: NMDME},
year={2022},
pages={362-366},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011912200003613},
isbn={978-989-758-630-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on New Media Development and Modernized Education - Volume 1: NMDME
TI - Information Management System of Student Laboratory Based on BP Neural Network
SN - 978-989-758-630-9
AU - Sun Y.
PY - 2022
SP - 362
EP - 366
DO - 10.5220/0011912200003613
PB - SciTePress