Strength prediction of similar materials to ionic rare earth ores based on orthogonal test and back propagation neural network
文献类型：期刊论文
| 作者 | Zhong, Wen; Deng, Yunchuan; Tenreiro Machado, J. A.; Zhang, Chao; Zhao, Kui; Wang, Xiaojun |
| 刊名 | SOFT COMPUTING
 |
| 出版日期 | 2019 |
| 卷号 | 23期号:19页码:9429-9437 |
| 关键词 | Back propagation neural network (BPNN) Orthogonal test Ionic rare earth (IRE) Similar materials (SM) Strength prediction |
| ISSN号 | 1432-7643 |
| DOI | 10.1007/s00500-019-03833-7 |
| 英文摘要 | This paper aims to predict the strength of materials similar to the ionic rare earth (IRE) ores [hereinafter referred as similar materials (SM)]. A 4 x Y x 2 back propagation neural network (BPNN) prediction model, based on 18 groups of samples of the SM with different mix proportions, was used to describe their strength. The BPNN modelling scheme includes four input layer neurons, representing the amounts of kaolinite, potassium feldspar, anorthose and mica, and two output layer neurons corresponding to the strength indices c and phi of the samples after 6 h leaching. Comparing the training and prediction errors, it is verified that the error in predicted strength is minimized when the number of hidden layer neurons Y equals 9. The correlation coefficient R of the prediction model is as high as 0.998, and the maximum relative errors of the strength indices (c and phi) are 4.11% and 4.26%, respectively. Orthogonal tests show that the BPNN is a reliable and accurate method to predict the strength of SM. Featuring uniform dispersion, comparability and nonlinear optimization, the proposed method sheds further light on the strength prediction of IRE ores. |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000485039900026 |
| 源URL | [http://119.78.100.198/handle/2S6PX9GI/14877]  |
| 专题 | 岩土力学所知识全产出_期刊论文 国家重点实验室知识产出_期刊论文 |
| 作者单位 | 1.Jiangxi Univ Sci & Technol, Sch Resources & Environm Engn, Ganzhou 341000, Peoples R China; 2.Chinese Acad Sci, Inst Rock & Soil Mech, State Key Lab Geomech & Geotech Engn, Beijing 430071, Peoples R China; 3.Chongyi Zhangyuan Tungsten Co Ltd, Ganzhou 341300, Peoples R China; 4.Polytech Porto, Inst Engn, Dept Elect Engn, P-4249015 Porto, Portugal |
| 推荐引用方式 GB/T 7714 | Zhong, Wen,Deng, Yunchuan,Tenreiro Machado, J. A.,et al. Strength prediction of similar materials to ionic rare earth ores based on orthogonal test and back propagation neural network[J]. SOFT COMPUTING,2019,23(19):9429-9437. |
| APA | Zhong, Wen,Deng, Yunchuan,Tenreiro Machado, J. A.,Zhang, Chao,Zhao, Kui,&Wang, Xiaojun.(2019).Strength prediction of similar materials to ionic rare earth ores based on orthogonal test and back propagation neural network.SOFT COMPUTING,23(19),9429-9437. |
| MLA | Zhong, Wen,et al."Strength prediction of similar materials to ionic rare earth ores based on orthogonal test and back propagation neural network".SOFT COMPUTING 23.19(2019):9429-9437. |
入库方式： OAI收割
来源：武汉岩土力学研究所
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