Study of frosting diagnosis with back propagation network and elman network

Siyu Han, Jiang Shen, Kaiyong Hu*, Jingyu Zhu, Xinghua Liu, Tingting Zhu, Dequan Zhang

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

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Abstract

Frosting diagnosis is important in refrigeration system control. This paper focuses on frosting diagnosis in a single-stage vapor compression refrigeration system. According to the reduction of refrigerating capacity, the experimental data of frosting can be divided into three categories: light, moderate and severe frosting. Then, two frosting prediction models are established with Back Propagation neural network (BPNN) model and Elman neural network (ENN) model. The simulation results illustrate that the overall diagnostic performance of the ENN is better than that of BPNN. The mean squared error (MSE) should be paid more attention for affecting the accuracy of models. The presented method in this paper can provide technical reference for frosting prediction.

Original languageEnglish
Article number012183
JournalIOP conference series: Earth and environmental science
Volume621
Issue number1
DOIs
Publication statusPublished - 22 Jan 2021
Externally publishedYes
Event5th International Conference on Renewable Energy and Environmental Protection, ICREEP 2020 - Shenzhen, Virtual, China
Duration: 23 Oct 202025 Oct 2020
Conference number: 5

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