[1]林毅,蔡福营,袁宇熹,等.PCA-SVM模型在几丁质酶最适温度建模中的应用[J].华侨大学学报(自然科学版),2008,29(2):236-240.[doi:10.11830/ISSN.1000-5013.2008.02.0236]
　LIN Yi,CAI Fu-ying,YUAN Yu-xi,et al.A Uniform Design Based PCA-SVM Model for Predicting Optimum Temperature in Chitinase[J].Journal of Huaqiao University(Natural Science),2008,29(2):236-240.[doi:10.11830/ISSN.1000-5013.2008.02.0236]
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PCA-SVM模型在几丁质酶最适温度建模中的应用()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第29卷
期数:
2008年第2期
页码:
236-240
栏目:
出版日期:
2008-04-20

文章信息/Info

Title:
A Uniform Design Based PCA-SVM Model for Predicting Optimum Temperature in Chitinase
文章编号:
1000-5013(2008)02-0236-05
作者:
林毅; 蔡福营; 袁宇熹; 张光亚
华侨大学材料科学与工程学院; 华侨大学材料科学与工程学院 福建泉州362021; 福建泉州362021
Author(s):
LIN Yi;  CAI Fu-ying;  YUAN Yu-xi;  ZHANG Guang-ya
College of Material Science and Engineering, Huaqiao University, Quanzhou 362021, China
关键词:
几丁质酶;  数学模型;  主成分分析;  支持向量机;  最适温度
Keywords:
principle component analysis;  support vector machine;  chitinase;  optimum temperature
分类号:
Q55
DOI:
10.11830/ISSN.1000-5013.2008.02.0236
文献标志码:
A
摘要:
采用主成分分析法(PCA)对样本数据集进行预处理,将得到的新样本数据集输入支持向量机(SVM),籍助均匀设计(UD),构建几丁质酶氨基酸组成和最适温度的数学模型.当径向基核函数的3个参数,惩罚系数C为10,ε为0.5,γ为5时,模型对温度拟合的平均绝对百分比误差为5.06%,预测的平均绝对误差为1.83℃,说明具有良好的预测效果且优于神经网络的预测结果.
Abstract:
The principal component analysis was applied to the data processing in training sets,the new principal components were then used as input data of support vector machine modle.A prediction model for optimum temperature of chitinase was established based on uniform design.When the regularized constant C,ε and γ were 10,0.5 and 5,respectively,the calculated temperature fitted the reported optimum temperature of chitinase very well and the mean absolute percent error(MAPEs) was 5.06%.At the same time,the predicted temperature fitted the reported optimum temperature well and the mean absolute error(MAE) was 1.83 ℃.It was superior in fittings and predictions compared to the model based on back propagation neural network.

参考文献/References:

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备注/Memo

备注/Memo:
国家自然科学基金资助项目(40601046); 福建省高等学校新世纪优秀人才计划支持项目(2006); 福建省自然科学基金资助项目(B0510011)
更新日期/Last Update: 2014-03-23