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This paper describes a mathematical model for the calciner outlet temperature via regression analysis, aim to the outlet temperature control of calciner. Based on the analysis of cement production, we find the tertiary air temperature, coal feeding and raw material feeding may affect the calciner outlet temperature. Firstly, we provide a Single-Input and Single-Output (SISO) mathematical model and...
Water consumption of plants is a key parameter for formulating irrigation system, and the precise prediction play a important role in improving the use efficiency of limited water resources. In this experiment, by using the method of artificial neural network and MATLAB DATA PROCESSING SYSTEM combined with the meteorological data of air temperature, relative air humidity, solar radiation, wind speed,...
In order to forecast industrial-waste-emissions much more accurate, we choose BP neural network and multiple linear regression to improve the precision of forecasting. The results of this comparison is that the BP neural network is distinctly superior. It was also proved that the model was feasible and easy to use, and had high accuracy.
This research aims at introducing a system independent method for scour and air entrainment prediction utilizing Artificial Neural Network (ANN) based on previous experimental plunge pool scour tests for inclined circular jets. Furthermore, the current manuscript introduced a single ANN model to predict air entrainment devoid of pre-knowledge of the jet condition either smooth or rough jet. Regarding...
The forecast of air passenger flow plays an important role in the management of airline, but the traditional forecast methods can't guarantee the generalization capability when they face a large-scale, multi-dimension, nonlinear and non-normal distribution time series data. To improve the forecast ability of air passenger flow, the SVM regression algorithm is introduced in this paper. By selecting...
To improve the predictive ability of a fuzzy neural network prediction model, the re-selection is made, by means the rough set attribute reduction, of the correlated prognostic factors that have been chosen and the re-selected factors are treated by blurring as model input, thereby establishing a new-type fuzzy neural network predictive model. Experiments are conducted for approximately two months...
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