ICCAS OpenIR
Predicting chain dimensions from an artificial neural network model
Zhang, LX; Xia, A; Zhao, DL
2000-12-01
Source PublicationJOURNAL OF POLYMER SCIENCE PART B-POLYMER PHYSICS
ISSN0887-6266
Volume38Issue:23Pages:3163-3167
AbstractArtificial neural network models are used to investigate polymer chain dimensions. In our model, the input nodes are glass transition temperature (T-g), entanglement molecular weight (M-e), and melt density (rho). The number of nodes in the hidden layer is eight. We found that the relative error for prediction of the characteristic ratio ranges from 0.77 to 7.5% and that the overall average error is 3.57%. Artificial neural network models may provide a new method for studying statistics properties of polymer chains. (C) 2000 John Wiley & Sons, Inc.
KeywordArtificial Neural Network Characteristic Ratio Polymers
Indexed BySCI
Language英语
WOS IDWOS:000165258000016
PublisherJOHN WILEY & SONS INC
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Document Type期刊论文
Identifierhttp://ir.iccas.ac.cn/handle/121111/75680
Collection中国科学院化学研究所
Corresponding AuthorZhang, LX
Affiliation1.Zhejiang Univ, Dept Phys, Hangzhou 310028, Peoples R China
2.Chinese Acad Sci, Inst Chem, Ctr Mol Sci, Polymer Phys Lab, Beijing 100080, Peoples R China
Recommended Citation
GB/T 7714
Zhang, LX,Xia, A,Zhao, DL. Predicting chain dimensions from an artificial neural network model[J]. JOURNAL OF POLYMER SCIENCE PART B-POLYMER PHYSICS,2000,38(23):3163-3167.
APA Zhang, LX,Xia, A,&Zhao, DL.(2000).Predicting chain dimensions from an artificial neural network model.JOURNAL OF POLYMER SCIENCE PART B-POLYMER PHYSICS,38(23),3163-3167.
MLA Zhang, LX,et al."Predicting chain dimensions from an artificial neural network model".JOURNAL OF POLYMER SCIENCE PART B-POLYMER PHYSICS 38.23(2000):3163-3167.
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