Application of back propagation neural network optimized by different algorithms in prediction of Mooney viscosity of ethylene-propylene-diene monomer compound
Genetic algorithm (GA) and particle swarm optimization (PSO) were used to optimize the back propagation（BP） neural network to establish the prediction model of Mooney viscosity of ethylene-propylene-diene monomer (EPDM) compound, and the error of the prediction results was compared and analyzed. The...
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| Format: | Article |
| Language: | zho |
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Editorial Office of China Synthetic Rubber Industry
2024-12-01
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| Series: | Hecheng xiangjiao gongye |
| Subjects: | |
| Online Access: | http://hcxjgy.paperopen.com/oa/DArticle.aspx?type=view&id=202306007 |
| _version_ | 1797367825621319680 |
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| author | LI Gao-wei, LI Jia, ZHU Jin-mei, JIAN Ran-ran, MIAO Qing, ZENG Xian-kui |
| author_facet | LI Gao-wei, LI Jia, ZHU Jin-mei, JIAN Ran-ran, MIAO Qing, ZENG Xian-kui |
| author_sort | LI Gao-wei, LI Jia, ZHU Jin-mei, JIAN Ran-ran, MIAO Qing, ZENG Xian-kui |
| collection | DOAJ |
| description | Genetic algorithm (GA) and particle swarm optimization (PSO) were used to optimize the back propagation（BP） neural network to establish the prediction model of Mooney viscosity of ethylene-propylene-diene monomer (EPDM) compound, and the error of the prediction results was compared and analyzed. The results showed that the predicted va-lues of the BP neural network model optimized by the two algorithms all maintained a high degree of fit and correlation with the measured values. Compared with the single BP neural network, the accuracy of the GA-BP neural network prediction model increased by 58.9% and the accuracy of the PSO-BP model increased by 3.57%, which indicated that the prediction accuracy of the prediction model optimized by the two algorithms, especially the BP neural network prediction model optimized by GA, improved significantly Mooney viscosity of EPDM compound.
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| first_indexed | 2024-03-08T17:23:39Z |
| format | Article |
| id | doaj.art-54256548278443f292a86e9f8977db31 |
| institution | Directory Open Access Journal |
| issn | 1000-1255 |
| language | zho |
| last_indexed | 2024-03-08T17:23:39Z |
| publishDate | 2024-12-01 |
| publisher | Editorial Office of China Synthetic Rubber Industry |
| record_format | Article |
| series | Hecheng xiangjiao gongye |
| spelling | doaj.art-54256548278443f292a86e9f8977db312024-01-03T01:56:39ZzhoEditorial Office of China Synthetic Rubber IndustryHecheng xiangjiao gongye1000-12552024-12-0146648849410.19908/j.cnki.ISSN1000-1255.2023.06.0488Application of back propagation neural network optimized by different algorithms in prediction of Mooney viscosity of ethylene-propylene-diene monomer compoundLI Gao-wei, LI Jia, ZHU Jin-mei, JIAN Ran-ran, MIAO Qing, ZENG Xian-kui0"School of Mechanical and Electrical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China"Genetic algorithm (GA) and particle swarm optimization (PSO) were used to optimize the back propagation（BP） neural network to establish the prediction model of Mooney viscosity of ethylene-propylene-diene monomer (EPDM) compound, and the error of the prediction results was compared and analyzed. The results showed that the predicted va-lues of the BP neural network model optimized by the two algorithms all maintained a high degree of fit and correlation with the measured values. Compared with the single BP neural network, the accuracy of the GA-BP neural network prediction model increased by 58.9% and the accuracy of the PSO-BP model increased by 3.57%, which indicated that the prediction accuracy of the prediction model optimized by the two algorithms, especially the BP neural network prediction model optimized by GA, improved significantly Mooney viscosity of EPDM compound. http://hcxjgy.paperopen.com/oa/DArticle.aspx?type=view&id=202306007back propagation neural networkgenetic algorithmparticle swarm optimizationethylene-propylene-diene monomercompoundmooney viscosityforecast model |
| spellingShingle | LI Gao-wei, LI Jia, ZHU Jin-mei, JIAN Ran-ran, MIAO Qing, ZENG Xian-kui Application of back propagation neural network optimized by different algorithms in prediction of Mooney viscosity of ethylene-propylene-diene monomer compound Hecheng xiangjiao gongye back propagation neural network genetic algorithm particle swarm optimization ethylene-propylene-diene monomer compound mooney viscosity forecast model |
| title | Application of back propagation neural network optimized by different algorithms in prediction of Mooney viscosity of ethylene-propylene-diene monomer compound |
| title_full | Application of back propagation neural network optimized by different algorithms in prediction of Mooney viscosity of ethylene-propylene-diene monomer compound |
| title_fullStr | Application of back propagation neural network optimized by different algorithms in prediction of Mooney viscosity of ethylene-propylene-diene monomer compound |
| title_full_unstemmed | Application of back propagation neural network optimized by different algorithms in prediction of Mooney viscosity of ethylene-propylene-diene monomer compound |
| title_short | Application of back propagation neural network optimized by different algorithms in prediction of Mooney viscosity of ethylene-propylene-diene monomer compound |
| title_sort | application of back propagation neural network optimized by different algorithms in prediction of mooney viscosity of ethylene propylene diene monomer compound |
| topic | back propagation neural network genetic algorithm particle swarm optimization ethylene-propylene-diene monomer compound mooney viscosity forecast model |
| url | http://hcxjgy.paperopen.com/oa/DArticle.aspx?type=view&id=202306007 |
| work_keys_str_mv | AT ligaoweilijiazhujinmeijianranranmiaoqingzengxiankui applicationofbackpropagationneuralnetworkoptimizedbydifferentalgorithmsinpredictionofmooneyviscosityofethylenepropylenedienemonomercompound |