基于相关向量机的钴铝层状双氢氧化物氟离子吸附性能预测
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- 英文论文题名：Predicting the performances of Removing Fluoride Ions of Co-AI Layered Double Hydroxides based on Relevance Vector Machine
- 论文作者：卢凯亮 ; 张庆 ; 徐潇 ; 纪晓波 ; 陆文聪
- 英文论文作者：Kailiang Lu ; Qing Zhang ; Xiao Xu ; Xiaobo Ji ; Wencong Lu ; Department of Chemistry ; College of Sciences ; Shanghai University
- 年：2016
- 作者机构：上海大学理学院化学系;
- 论文关键词：相关向量回归(RVM) ; 层状双氢氧化物 ; 吸附性能
- 英文论文关键词：Relevance Vector Machine for Regression Method ; Layered Double Hydroxides ; Adsorptive property
- 会议召开时间：2016-07-01
- 会议录名称：中国化学会第30届学术年会摘要集-第二十五分会：化学信息学与化学计量学
- 语种：中文
- 分类号：O647.3
- 学会代码：ZGHY
- 会议名称：中国化学会第30届学术年会-第二十五分会 ; 化学信息学与化学计量学
- 会议地点：中国辽宁大连
- 主办单位：中国化学会
- 学会名称：中国化学会
- 页数：1
- 文件大小：295k
- 原文格式：D
- 会议级别：全国
摘要
采用相关向量机(RVM)算法~([1]),构建了钴铝层状双氢氧化物氟离子的吸附性能与材料制备条件之间的非线性关系~([2])。样本集留一法交叉验证结果表明该材料氟离子吸附性能预测值与实验值间的均方根误差(RMSE)和相关系数(r)分别为0.864和0.948。本工作为该类材料提供了基于材料制备条件的氟离子吸附性能预报有效方法。
The QSPR model for predicting the performances of removing fluoride ions of Co-Al Layered Double Hydroxides was constructed by using experimental conditions-relevance vector machine(RVM).The results show that the RMSE(Root Mean Square Error) and r(correlation coefficient)between the predicted and experimental values for the LOOCV(leave one out cross validation) of sample set are 0.864 and 0.948 respectively.It can be concluded that the RVM method was an effective way to predict the performances of removing fluoride ions for Co-Al Layered Double Hydroxides.
The QSPR model for predicting the performances of removing fluoride ions of Co-Al Layered Double Hydroxides was constructed by using experimental conditions-relevance vector machine(RVM).The results show that the RMSE(Root Mean Square Error) and r(correlation coefficient)between the predicted and experimental values for the LOOCV(leave one out cross validation) of sample set are 0.864 and 0.948 respectively.It can be concluded that the RVM method was an effective way to predict the performances of removing fluoride ions for Co-Al Layered Double Hydroxides.
引文
[1]Tipping,ME(2001)Journal of Machine Learning Research,1,211-244.
[2]Zhao,X.,Zhang,L.,Xiong,P.,Ma,W.,Qian,N.,&Lu,W.(2015).Microporous and Mesoporous Materials,201,91-98.
[2]Zhao,X.,Zhang,L.,Xiong,P.,Ma,W.,Qian,N.,&Lu,W.(2015).Microporous and Mesoporous Materials,201,91-98.