Identification of Flow Ｒegimes Based on Adaptive Learning and Additional Momentum BP Neural Network for Electrical Capacitance Tomography
Traditional BP neural network is a typical mehtod to solve ECT system of flow pattern identification. It is applied to the simple problems in industrial applications，but there are many defects in solving complex industrial problems. In this paper based on the analysis of deficiency of BP neural ne...
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| Format: | Article |
| Language: | zho |
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Harbin University of Science and Technology Publications
2018-02-01
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| Series: | Journal of Harbin University of Science and Technology |
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| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1488 |
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| _version_ | 1849225303037050880 |
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| author | WANG Li-li LIU Hong-bo CHEN De-yun FENG Qi-shuai |
| author_facet | WANG Li-li LIU Hong-bo CHEN De-yun FENG Qi-shuai |
| author_sort | WANG Li-li |
| collection | DOAJ |
| description | Traditional BP neural network is a typical mehtod to solve ECT system of flow pattern identification.
It is applied to the simple problems in industrial applications，but there are many defects in solving complex
industrial problems. In this paper based on the analysis of deficiency of BP neural network，for reducing the error
oscillation，the adaptive learning rate adjustment factor and the additional momentum is introduced. In this method，
the electrical capacitance values are input to train a network to identify the flow patterns. The simulation results
show the algorithm not only inherits the advantages of traditional BP neural network，but also improve slow
convergence and solve being prone to fall into local minimum problems in flow pattern identification of ECT system |
| format | Article |
| id | doaj-art-09c4e1b58e9741cd8b218c34b16b9bf0 |
| institution | Kabale University |
| issn | 1007-2683 |
| language | zho |
| publishDate | 2018-02-01 |
| publisher | Harbin University of Science and Technology Publications |
| record_format | Article |
| series | Journal of Harbin University of Science and Technology |
| spelling | doaj-art-09c4e1b58e9741cd8b218c34b16b9bf02025-08-25T03:13:36ZzhoHarbin University of Science and Technology PublicationsJournal of Harbin University of Science and Technology1007-26832018-02-01230110511010.15938/j.jhust.2018.01.019Identification of Flow Ｒegimes Based on Adaptive Learning and Additional Momentum BP Neural Network for Electrical Capacitance TomographyWANG Li-li0LIU Hong-bo1CHEN De-yun2FENG Qi-shuai3School of Computer Science and Technology，Harbin University of Science and Technology，Harbin 150080，ChinaSchool of Computer Science and Technology，Harbin University of Science and Technology，Harbin 150080，ChinaSchool of Computer Science and Technology，Harbin University of Science and Technology，Harbin 150080，ChinaSchool of Computer Science and Technology，Harbin University of Science and Technology，Harbin 150080，ChinaTraditional BP neural network is a typical mehtod to solve ECT system of flow pattern identification. It is applied to the simple problems in industrial applications，but there are many defects in solving complex industrial problems. In this paper based on the analysis of deficiency of BP neural network，for reducing the error oscillation，the adaptive learning rate adjustment factor and the additional momentum is introduced. In this method， the electrical capacitance values are input to train a network to identify the flow patterns. The simulation results show the algorithm not only inherits the advantages of traditional BP neural network，but also improve slow convergence and solve being prone to fall into local minimum problems in flow pattern identification of ECT systemhttps://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1488electrical capacitance tomographyflow regime identificationbp neural networklocal minimumconvergence speed |
| spellingShingle | WANG Li-li LIU Hong-bo CHEN De-yun FENG Qi-shuai Identification of Flow Ｒegimes Based on Adaptive Learning and Additional Momentum BP Neural Network for Electrical Capacitance Tomography Journal of Harbin University of Science and Technology electrical capacitance tomography flow regime identification bp neural network local minimum convergence speed |
| title | Identification of Flow Ｒegimes Based on Adaptive Learning and Additional Momentum BP Neural Network for Electrical Capacitance Tomography |
| title_full | Identification of Flow Ｒegimes Based on Adaptive Learning and Additional Momentum BP Neural Network for Electrical Capacitance Tomography |
| title_fullStr | Identification of Flow Ｒegimes Based on Adaptive Learning and Additional Momentum BP Neural Network for Electrical Capacitance Tomography |
| title_full_unstemmed | Identification of Flow Ｒegimes Based on Adaptive Learning and Additional Momentum BP Neural Network for Electrical Capacitance Tomography |
| title_short | Identification of Flow Ｒegimes Based on Adaptive Learning and Additional Momentum BP Neural Network for Electrical Capacitance Tomography |
| title_sort | identification of flow regimes based on adaptive learning and additional momentum bp neural network for electrical capacitance tomography |
| topic | electrical capacitance tomography flow regime identification bp neural network local minimum convergence speed |
| url | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=1488 |
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