Implementation of Vector Hysteresis Model Utilizing Enhanced Neural Network Based on Collaborative Algorithm
A hysteresis model, based on the enhanced neural network with parallel strategy, is put forward for the prediction of the accurate magnetic behavior of electrical steel sheets (ESSs). Aimed at overcoming the drawbacks such as low convergence rate and convenient to trap into local optimum in the conv...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9000843/ |
| _version_ | 1819158634766008320 |
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| author | Lianqiang Chi Dianhai Zhang Mengfan Jia Ziyan Ren |
| author_facet | Lianqiang Chi Dianhai Zhang Mengfan Jia Ziyan Ren |
| author_sort | Lianqiang Chi |
| collection | DOAJ |
| description | A hysteresis model, based on the enhanced neural network with parallel strategy, is put forward for the prediction of the accurate magnetic behavior of electrical steel sheets (ESSs). Aimed at overcoming the drawbacks such as low convergence rate and convenient to trap into local optimum in the conventional back-propagation neural network (BPNN), a novel collaborative BPNN learning algorithm is introduced according to the error back propagation mechanism and particle swarm optimization (PSO). The reasonable selection of the test point set by the uniform design of experiment methodology, has the potential of lowering the measurement cost, together with guaranteeing the accuracy of the hysteresis modeling. A parallel strategy, which is based on the fast Fourier transformation (FFT), is applied for enhancing the train efficiency of BPNNs. The proposed algorithm is applied for the purpose of modeling the vector hysteresis behavior of ESS. Together, the comparison of the measured and predicted results of H-locus and core loss is discussed as well. |
| first_indexed | 2024-12-22T16:27:47Z |
| format | Article |
| id | doaj.art-2b9e0684bb4443b08635e22918e94c72 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T16:27:47Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2b9e0684bb4443b08635e22918e94c722022-12-21T18:20:07ZengIEEEIEEE Access2169-35362020-01-018341623416910.1109/ACCESS.2020.29744079000843Implementation of Vector Hysteresis Model Utilizing Enhanced Neural Network Based on Collaborative AlgorithmLianqiang Chi0https://orcid.org/0000-0002-6797-0402Dianhai Zhang1https://orcid.org/0000-0003-1587-3966Mengfan Jia2https://orcid.org/0000-0003-1329-5402Ziyan Ren3https://orcid.org/0000-0001-8988-7290School of Electrical Engineering, Shenyang University of Technology, Shenyang, ChinaSchool of Electrical Engineering, Shenyang University of Technology, Shenyang, ChinaHunan Provincial Engineering Research Center for Electric Vehicle Motors, CRRC Zhuzhou Electric Company, Ltd., Zhuzhou, ChinaSchool of Electrical Engineering, Shenyang University of Technology, Shenyang, ChinaA hysteresis model, based on the enhanced neural network with parallel strategy, is put forward for the prediction of the accurate magnetic behavior of electrical steel sheets (ESSs). Aimed at overcoming the drawbacks such as low convergence rate and convenient to trap into local optimum in the conventional back-propagation neural network (BPNN), a novel collaborative BPNN learning algorithm is introduced according to the error back propagation mechanism and particle swarm optimization (PSO). The reasonable selection of the test point set by the uniform design of experiment methodology, has the potential of lowering the measurement cost, together with guaranteeing the accuracy of the hysteresis modeling. A parallel strategy, which is based on the fast Fourier transformation (FFT), is applied for enhancing the train efficiency of BPNNs. The proposed algorithm is applied for the purpose of modeling the vector hysteresis behavior of ESS. Together, the comparison of the measured and predicted results of H-locus and core loss is discussed as well.https://ieeexplore.ieee.org/document/9000843/Back-propagation neural networkcollaborative algorithmdesign of experimentparallel strategyparticle swarm optimizationvector hysteresis model |
| spellingShingle | Lianqiang Chi Dianhai Zhang Mengfan Jia Ziyan Ren Implementation of Vector Hysteresis Model Utilizing Enhanced Neural Network Based on Collaborative Algorithm IEEE Access Back-propagation neural network collaborative algorithm design of experiment parallel strategy particle swarm optimization vector hysteresis model |
| title | Implementation of Vector Hysteresis Model Utilizing Enhanced Neural Network Based on Collaborative Algorithm |
| title_full | Implementation of Vector Hysteresis Model Utilizing Enhanced Neural Network Based on Collaborative Algorithm |
| title_fullStr | Implementation of Vector Hysteresis Model Utilizing Enhanced Neural Network Based on Collaborative Algorithm |
| title_full_unstemmed | Implementation of Vector Hysteresis Model Utilizing Enhanced Neural Network Based on Collaborative Algorithm |
| title_short | Implementation of Vector Hysteresis Model Utilizing Enhanced Neural Network Based on Collaborative Algorithm |
| title_sort | implementation of vector hysteresis model utilizing enhanced neural network based on collaborative algorithm |
| topic | Back-propagation neural network collaborative algorithm design of experiment parallel strategy particle swarm optimization vector hysteresis model |
| url | https://ieeexplore.ieee.org/document/9000843/ |
| work_keys_str_mv | AT lianqiangchi implementationofvectorhysteresismodelutilizingenhancedneuralnetworkbasedoncollaborativealgorithm AT dianhaizhang implementationofvectorhysteresismodelutilizingenhancedneuralnetworkbasedoncollaborativealgorithm AT mengfanjia implementationofvectorhysteresismodelutilizingenhancedneuralnetworkbasedoncollaborativealgorithm AT ziyanren implementationofvectorhysteresismodelutilizingenhancedneuralnetworkbasedoncollaborativealgorithm |