In order to predict the 28 day compressive strength of coal gangues as clay for cement, a prediction method of grey model and radial basis function (GM-RBF) neural network combination model is presented according to the data of cement physics test analysis. The method makes used of the advantage of both GM and RBF neural network. Firstly, the combination model built up GM(1, N) network based on its test analysis data, and the data were preprocessed. One accumulated generation operation (1-AGO) and normalization were carried out. Predicted precision and scatter constant were set up to the combination model. Then, these processed samples served as the input vectors for RBF neural network, the measurement data of 28 day compressive strength served as output expectation value for model. Comparisons were carried out between prediction data and measurement data, then the data were adjusted logically. Finally, the GM-RBF neural network combination model is fit for precision requirement. AGO and pretreatment were used for data processing which can reduce randomness of training samples. It also shows that the method is self-adaptive, self-organized and fast. The model can not only avoid the theoretical error of GM(1,N), but also predict the further period compressive strength. The results show that it is better than GM(1,N) model or RBF neural network model. The combination model owns a fine agreement and adapts to predict cement strength. It can provide efficient reference of quality analysis for coal gangues as clay for cement.
QIU Guohua
,
SHENTU Nanying
,
SHI Zhenglun
. Prediction Method of Cement Strength Based on GM-RBF Neural Network Combination Model[J]. Science & Technology Review, 2014
, 32(3)
: 56
-61
.
DOI: 10.3981/j.issn.1000-7857.2014.03.008
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