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

doi:10.1088/1755-1315/621/1/012183

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 journal › Conference article › Academic › peer-review

1 Citation (Scopus)

30 Downloads (Pure)

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 language	English
Article number	012183
Journal	IOP conference series: Earth and environmental science
Volume	621
Issue number	1
DOIs	https://doi.org/10.1088/1755-1315/621/1/012183
Publication status	Published - 22 Jan 2021
Externally published	Yes
Event	5th International Conference on Renewable Energy and Environmental Protection, ICREEP 2020 - Shenzhen, Virtual, China Duration: 23 Oct 2020 → 25 Oct 2020 Conference number: 5

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10.1088_1755-1315_621Final published version, 773 KBLicence: CC BY

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title = "Study of frosting diagnosis with back propagation network and elman network",

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.",

author = "Siyu Han and Jiang Shen and Kaiyong Hu and Jingyu Zhu and Xinghua Liu and Tingting Zhu and Dequan Zhang",

note = "Funding Information: This work was financially supported by National Risk Assessment Laboratory of Agro-products Processing Quality and Safety, Ministry of Agriculture and Rural Affairs (S2020KFKT-06), the National Natural Science Foundation (Grant No.51906177). Publisher Copyright: {\textcopyright} Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.; 5th International Conference on Renewable Energy and Environmental Protection, ICREEP 2020, ICREEP 2020 ; Conference date: 23-10-2020 Through 25-10-2020",

year = "2021",

month = jan,

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doi = "10.1088/1755-1315/621/1/012183",

language = "English",

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journal = "IOP conference series: Earth and environmental science",

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TY - JOUR

T1 - Study of frosting diagnosis with back propagation network and elman network

AU - Han, Siyu

AU - Shen, Jiang

AU - Hu, Kaiyong

AU - Zhu, Jingyu

AU - Liu, Xinghua

AU - Zhu, Tingting

AU - Zhang, Dequan

N1 - Conference code: 5

PY - 2021/1/22

Y1 - 2021/1/22

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85100767046

U2 - 10.1088/1755-1315/621/1/012183

DO - 10.1088/1755-1315/621/1/012183

M3 - Conference article

AN - SCOPUS:85100767046

SN - 1755-1307

VL - 621

JO - IOP conference series: Earth and environmental science

JF - IOP conference series: Earth and environmental science

IS - 1

M1 - 012183

T2 - 5th International Conference on Renewable Energy and Environmental Protection, ICREEP 2020

Y2 - 23 October 2020 through 25 October 2020

ER -

Study of frosting diagnosis with back propagation network and elman network

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