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[求助]
请帮忙查一下论文是不是被SCI检索了，谢谢！
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如题，论文信息如下： Lv H, Liu N, Tian D, et al. Circuit-based neural network models for estimating the solubility of diosgenin[J]. Chemical Engineering Communications, 2019: 1-13. |
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aygxy_lhc: 金币+10, ★★★很有帮助, 谢谢了! 2020-06-29 20:29:35
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Circuit-based neural network models for estimating the solubility of diosgenin 作者:Lv, HC (Lv, Huichao)[ 1 ] ; Liu, NN (Liu, Nana)[ 1 ] ; Tian, DY (Tian, Dayong)[ 1 ] ; Zeng, YW (Zeng, Yuwen)[ 1 ] ; Li, BL (Li, Baoli)[ 1 ] CHEMICAL ENGINEERING COMMUNICATIONS DOI: 10.1080/00986445.2019.1663181 Early access icon在线发表日期: SEP 2019 文献类型:Article; Early Access 查看期刊影响力 摘要 Several new circuit-based neural network models were conceived and utilized to estimate the solubility of diosgenin. Six mixed alcohol solvents and a pure solvent (carbon tetrachloride) were selected as the model systems to demonstrate the point of interest. To make full use of the collected solubility data of diosgenin in these solvents, they were categorized into training, testing and validation sets and a 5-fold cross validation was adopted in the buildup of the model. The results of the statistical analysis and the sum of ranking differences method indicate the parallel-serial neural network model gives more accurate description of the solubility data of diosgenin in contrast to other patterns. It also outperforms two empirical equations in terms of calculating accuracy. In addition, this suggested model can exhibit the effect of the changes of the components and their proportions in solvent on the solution behavior of diosgenin correctly. 关键词 作者关键词:Circuit-based neural network; Diosgenin; Solubility; Mixed alcohol solvent; Carbon tetrachloride; Sum of ranking differences KeyWords Plus:HIGH-PRESSURE; PREDICTION; OPTIMIZATION; ADSORPTION; DENSITY; SYSTEM; WATER; DYES 作者信息 通讯作者地址: Lv, HC (corresponding author) Anyang Inst Technol, Sch Chem & Environm Engn, Anyang 455000, Peoples R China. 地址: [ 1 ] Anyang Inst Technol, Sch Chem & Environm Engn, Anyang 455000, Peoples R China 电子邮件地址:prolvhuichao@126.com 基金资助致谢 基金资助机构显示详情 授权号 National Natural Science Foundation of China U1404217 Key Science and Technology Project of Henan Province 172102310166 查看基金资助信息 出版商 TAYLOR & FRANCIS INC, 530 WALNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 类别 / 分类 研究方向:Engineering Web of Science 类别:Engineering, Chemical 文献信息 语言:English 入藏号: WOS:000486174500001 ISSN: 0098-6445 eISSN: 1563-5201 其他信息 IDS 号: IY1TL Web of Science 核心合集中的 "引用的参考文献": 30 Web of Science 核心合集中的 "被引频次": 0 |
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