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Content Provider | IEEE Xplore Digital Library |
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Author | Shuxia Ren Dan Yang Fengqiu Ji Xiushu Tian |
Copyright Year | 2010 |
Description | Author affiliation: Computer department, Shijiazhuang People's Armed Police Command College, 05000, China (Dan Yang) || School of material science and engineering, Shijiazhuang TieDao University, 050043, China (Shuxia Ren; Fengqiu Ji; Xiushu Tian) |
Abstract | Different modelling methods based on neural networks have become popular and been widely used in a great variety of fields. The properties of cements were predicted by neural network according to their chemical compositions, fineness, and other factors in this paper. The results showed that generalized regression neural network (GRNN) has higher accuracy and faster training speed compared with BP neural network. The maximum relative errors of the hydration heat and compressive strength predicted by GRNN were less than 5% and 9%, respectively. |
File Size | 1442796 |
File Format | |
ISBN | 9781424471645 |
e-ISBN | 9781424471645 |
DOI | 10.1109/ICCDA.2010.5541402 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2010-06-25 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Hydration heat Cement industry Transfer functions Predictive models Educational institutions Compressive strength Application software Chemicals Materials science and technology Design engineering Generalized regression neural network Neural networks Computer networks |
Content Type | Text |
Resource Type | Article |
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