This paper studies the correlation between Traditional Chinese Medicine(TCM)Constitution discrimination and physical examination index based on bpnn algorithm.253cases of routine urine test were used to build a linkag...
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This paper studies the correlation between Traditional Chinese Medicine(TCM)Constitution discrimination and physical examination index based on bpnn algorithm.253cases of routine urine test were used to build a linkage model between TCM Constitution and physical indicators via bpnn *** to the test,the correct rate of learning and test group are60%and40%,respectively.A strong correlation was found between TCM Constitution and physical examination *** applying cutting-edge knowledge and technologies,the development and modernization process of TCM can be greatly promoted.
This study seeks to improve urban supply chain management and collaborative governance in the context of public health emergencies (PHEs) by integrating fuzzy theory with the Back Propagation Neural Network (bpnn) alg...
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This study seeks to improve urban supply chain management and collaborative governance in the context of public health emergencies (PHEs) by integrating fuzzy theory with the Back Propagation Neural Network (bpnn) algorithm. By combining these two approaches, an early warning mechanism for supply chain risks during PHEs is developed. The study employs Matlab software to simulate supply chain risks, incorporating fuzzy inference techniques with the adaptive data modeling capabilities of neural networks for both training and testing. The results demonstrate that the proposed model effectively identifies factors contributing to supply chain deterioration, with a warning error as low as 0.001, significantly enhancing the accuracy and timeliness of demand forecasting. The bpnn algorithm, through its self-learning and adaptive features, facilitates dynamic optimization and precise scheduling across various stages of the supply chain. This capability is particularly valuable in addressing challenges associated with sudden demand spikes and resource allocation. As a result, the mechanism is able to accurately and promptly identify adverse trends in the supply chain, thereby enhancing the efficiency and flexibility of urban emergency responses, mitigating risks, and offering both theoretical and practical contributions to urban collaborative governance.
In recent years,with the wide application of image data visual extraction technology in the field of industrial engineering,the development of industrial economy has reached a new *** explore the interaction between t...
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In recent years,with the wide application of image data visual extraction technology in the field of industrial engineering,the development of industrial economy has reached a new *** explore the interaction between the pellet microstructure and compressive strength,firstly,the pellet microstructure needed for the experiment was obtained using a Leica DM4500P *** area proportions of hematite,calcium ferrite,magnetite,calcium silicate and pore in pellet microstructure were extracted by visual extraction technology of image ***,the relationship between the area proportions of mineral components and compressive strength was established by backpropagation neural network(bpnn),generalized regression neural network(GRNN)and beetle antennae search-generalized regression neural network(BAS-GRNN)algorithms,which proves that the pellet microstructure can be used as the prediction standard of compressive *** errors of bpnn and BAS-GRNN are 5.13%and 3.37%,respectively,both of which are less than 5.5%.Therefore,through data visualization,we are able to discuss the connection between various components of pellet microstructure and compressive strength and provide new research ideas for improving the compressive strength and metallurgical performance of pellet.
Blast furnace smelting is a traditional iron-making process. Its product, hot metal, is an important raw material for the production of steel. Steelmaking efficiency can be improved and steel product quality can be st...
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Blast furnace smelting is a traditional iron-making process. Its product, hot metal, is an important raw material for the production of steel. Steelmaking efficiency can be improved and steel product quality can be stabilized by using proper hot metal. Sulfur is an important indicator reflecting the quality of hot metal, it is necessary to establish an accurate prediction model to predict the sulfur content of hot metal, to effectively guide the production process. There is a non-linear relationship among the factors influencing the desulfurization effect during the blast furnace smelting process, and the back propagation neural network (bpnn) model has a strong ability to solve nonlinear problems. However, bpnn has the disadvantages of slow convergence speed and easy to fall into local minima. To improve the prediction accuracy, an improved algorithm combining Kmeans and bpnn is proposed in this paper. The study showed that compared with the bpnn model and case-based reasoning (CBR) model, the Kmeans-bpnn model has the lowest RMSE and MAPE values, which indicates a high degree of fit and a low degree of dispersion. The Kmeans-bpnn model has the largest HR value, which indicates the highest prediction accuracy. The proposed Kmeans-bpnn prediction model achieves a hit rate of 96%, which is 4.5% higher than before the improvement. It can effectively improve the prediction accuracy of hot metal sulfur content.
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