In order to predict blended coal's property accurately, a new kind of hybrid prediction model based on principal component analysis (PCA) and support vector machine (SVM) was established. PCA was used to transform...In order to predict blended coal's property accurately, a new kind of hybrid prediction model based on principal component analysis (PCA) and support vector machine (SVM) was established. PCA was used to transform the high-dimensional and correlative influencing factors data to low-dimensional principal component subspace. Well-trained SVM was used to extract influencing factors as input to predict blended coal's property. Then experiments were made by using the real data, and the results were compared with weighted averaging method (WAM) and BP neural network. The results show that PCA-SVM has higher prediction accuracy in the condition of few data, thus the hybrid model is of great use in the domain of power coal blending.展开更多
China has long been a coal-based energyconsumption country.The coal's combustion process andits particle size are closely related.Because there are stilldifficulties in understanding and mastering the energyconsum...China has long been a coal-based energyconsumption country.The coal's combustion process andits particle size are closely related.Because there are stilldifficulties in understanding and mastering the energyconsumption of comminution,the economic fineness tobalance comminution and burning is mainly obtainedaccording to experience.With the increasingly wide andextensive use of coal,the energy consumption of coalcomminution has been paid more and more attention.Inthis paper,the research on energy consumption ofcomminution is analyzed and summarized to provide areference for the energy consumption of coalcomminution.展开更多
基金Project(50579101) supported by the National Natural Science Foundation of China
文摘In order to predict blended coal's property accurately, a new kind of hybrid prediction model based on principal component analysis (PCA) and support vector machine (SVM) was established. PCA was used to transform the high-dimensional and correlative influencing factors data to low-dimensional principal component subspace. Well-trained SVM was used to extract influencing factors as input to predict blended coal's property. Then experiments were made by using the real data, and the results were compared with weighted averaging method (WAM) and BP neural network. The results show that PCA-SVM has higher prediction accuracy in the condition of few data, thus the hybrid model is of great use in the domain of power coal blending.
文摘China has long been a coal-based energyconsumption country.The coal's combustion process andits particle size are closely related.Because there are stilldifficulties in understanding and mastering the energyconsumption of comminution,the economic fineness tobalance comminution and burning is mainly obtainedaccording to experience.With the increasingly wide andextensive use of coal,the energy consumption of coalcomminution has been paid more and more attention.Inthis paper,the research on energy consumption ofcomminution is analyzed and summarized to provide areference for the energy consumption of coalcomminution.
