The Exploratory Research Using BP Neural Network to Analyze the Influencing Factors of Hospitalization Expenses in Acute Appendicitis

2012 ◽  
pp. 1355-1359
Author(s):  
Jianhui Wu ◽  
Jie Tang ◽  
Guoli Wang ◽  
Sufeng Yin
Keyword(s):  
Neural Network ◽  
Acute Appendicitis ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Exploratory Research

scholarly journals Research on Substation Project Cost Prediction Based on Sparrow Search Algorithm Optimized BP Neural Network

2021 ◽  
Vol 13 (24) ◽  
pp. 13746
Author(s):  
Xiaomin Xu ◽  
Luyao Peng ◽  
Zhengsen Ji ◽  
Shipeng Zheng ◽  
Zhuxiao Tian ◽  
...  
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Search Algorithm ◽  
Power Grid ◽  
Engineering System ◽  
Project Cost ◽  
Total Investment ◽  
Engineering Cost

The prediction of power grid engineering cost is the basis of fine management of power grid engineering, and accurate prediction of substation engineering cost can effectively ensure the fine operation of engineering funds. With the continuous expansion of the engineering system, the influencing factors and data dimensions of substation project investment are gradually diversified and complex, which further increases the uncertainty and complexity of substation project cost. Based on the concept of substation engineering data space, this paper investigates the influencing factors and constructs the static total investment intelligent prediction model of substation engineering. The emerging swarm intelligence algorithm, sparrow search algorithm (SSA), is used to optimize the parameters of the BP neural network to improve the prediction accuracy and convergence speed of neural network. In order to test the validity of the model, an example analysis is carried out based on the data of a provincial substation project. It was found that the SSA-BP can effectively improve the prediction accuracy and provide new methods and approaches for practical application and research.

Performance Analysis of Bentonite-PVA Fiber Cement-Based Composites for Building Based on BP Neural Network

2020 ◽  
Vol 852 ◽  
pp. 209-219
Author(s):  
Zhe Shen
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Thermal Conductivity ◽  
Composite Materials ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Analysis Method ◽  
System Input ◽  
Prediction And Simulation ◽  
Pva Fiber

The paper will use BP neural network analysis method to study the thermal conductivity of bentonite and its influencing factors as a system. The heat conduction of bentonite was used as the output of the system, and its influencing factors were used as the system input to simulate. The corresponding simulation model was established to verify the thermal conductivity data. In addition, the analysis of the mechanical properties of the bentonite-PVA fiber cement-based composite materials for construction has not only laid a theoretical and realistic foundation for the prediction and simulation of the thermal conductivity of bentonite, but also has opened up the mechanical properties of the bentonite-PVA fiber cement-based composite materials a new path.

Scientific Research Project Cost Estimating Method and System Based on Improved BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 1696-1700 ◽  
Author(s):  
Yi Lin Wang ◽  
Guo Xin Wang ◽  
Yan Yan
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Scientific Research ◽  
Cost Estimating ◽  
Research Project ◽  
Project Cost ◽  
Model Based ◽  
Scientific Research Project ◽  
Testing Experiment

Traditional scientific research project cost estimating method cannot meet accuracy and practicability at the same time. Aiming at this problem, scientific research project cost estimating method based on neural network was built. Firstly, the construction and influencing factors of scientific research project cost were analyzed. Secondly, an estimating model based on improved BP neural network was built; a nonlinear expression between influencing factors (input) and cost (output) was created. Finally, an estimating system with the model was implemented by Java. The effectiveness of the method was tested. Testing experiment showed the estimating model based on improved BP neural network is reliable and the precision is high.

Prediction for Percentage of Vehicle Entering Expressway Rest Area Based on BP Neural Network

2012 ◽  
Vol 178-181 ◽  
pp. 1956-1960
Author(s):  
Xiao Yan Shen ◽  
Hao Xue Liu ◽  
Jia Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Influencing Factors ◽  
High Precision ◽  
Bp Neural Network ◽  
High Speed ◽  
High Speed Railway ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Influence Degree

In order to scientifically decide the percentage of vehicle entering expressway rest area, based on analyzing the influencing factors relating to the percent of mainline traffic stopping, a BP neural network prediction model for it was put forward. Finally, The Xinzheng Rest Area (XRA) was taken as an example for verifying the feasibility of the prediction model and determining the influence degree of the Shijiazhuang-Wuhan high-speed railway on the percentage of mainline vehicles entering XRA. The result shows that the model had a high precision and reliability.

scholarly journals Influencing Factors and Quality Evaluation of Urban Thermal Environment Based on Artificial Neural Network

2021 ◽  
Vol 39 (1) ◽  
pp. 128-136
Author(s):  
Liangwei Zhao ◽  
Xiaowei Li ◽  
Xuguang Chai
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Land Surface ◽  
Quality Evaluation ◽  
Thermal Environment ◽  
Island Effect ◽  
Urban Heat ◽  
Urban Thermal Environment ◽  
Mlr Model

To alleviate the urban heat island effect that is getting increasingly serious these days, the research on the monitoring, simulation, and regulation of the thermal environment of cities has become a necessity. Aiming at figuring out the correlations between influencing factors and giving accurate quality evaluation of urban thermal environment, this study extracted 10 influencing factors of urban thermal environment and gave their influence, and then performed the Land Surface Temperature (LST) retrieval of a target city. After that, this paper constructed a Multiple Linear Regression (MLR) model and explored the law of the numerical changes of the influencing factors of urban thermal environment. At last, this paper also built a BP neural network to predict the quality evaluation of urban thermal environment and used experimental results to prove the effectiveness of the proposed algorithm and model.

Optimization Research of the Comprehensive Coke Rate of Blast Furnace Based on the Operational Characteristic of Auxiliary Materials

2012 ◽  
Vol 260-261 ◽  
pp. 3-9 ◽  
Author(s):  
Hua Zhang ◽  
Zhao Hui Feng ◽  
Yan Hong Wang
Keyword(s):  
Neural Network ◽  
Blast Furnace ◽  
Influencing Factors ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Operational Characteristic ◽  
Coke Rate ◽  
New Perspective ◽  
Bp Neural Network Model

With the in-depth study on the blast furnace iron-making process and the operational characteristic of auxiliary materials in iron-making process, the comprehensive coke rate’s main influencing factors based on the operation characteristics of auxiliary materials were found. Then, a BP neural network model was used to simulate the mathematic mapping relationship between comprehensive coke rate and main influencing factors. Based on the established BP neural network model, through setting the comprehensive coke rate lowest as the goal and using the actual production data of a iron &steel company’s 6# blast furnace ,a genetic algorithm method is adopted to find the best optimal combination among the main influencing factors. The results show that after optimization calculation the comprehensive coke rate could be reduced about 35.85kg. A new perspective and a scientific method are proposed to realize the target of energy conservation and emission reduction in ironmaking process in this paper.

Human-machine-environment information fusion and control compensation strategy for large optical mirror processing system

2020 ◽  
pp. 095440622095968
Author(s):  
Zujin Jin ◽  
Gang Cheng ◽  
Shibiao Chen ◽  
Feng Guo
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Information Fusion ◽  
Bp Neural Network ◽  
Environmental Control ◽  
Processing System ◽  
Parameter Analysis ◽  
Related Factors ◽  
Joint Friction ◽  
Forming Precision

Large optical mirrors require an ultra-precise machining equipment, and a high level of surface-forming precision must be achieved. However, optical mirror processing systems (OMPSs) are susceptible to human behaviors, mechanical structural errors, and processing environments. The factors that affect quality include artificially formulated processes, slurry choice, joint friction, force-induced deformation, ambient temperature, and vibration interference. These factors can lead to a decrease in the accuracy of an OMPS. To study the influence of disturbances in the human-machine-environment (HME) on the OMPS, it is necessary to conduct a fusion analysis of the related factors. A parameter analysis is first conducted on the HME factors that influence the accuracy of OMPS. Then, the factors that influence the accuracy most significantly are determined. Subsequently, with the influencing factors as input parameters, and the output forces of the computer-controlled optical surface (CCOS) grinding system as the output parameters, the HME influencing factors are fused through a BP neural network optimized using a genetic algorithm, and the result is compared with that resulting from the original BP neural network fusion. Finally, according to the results of the fusion, environmental control of the processing system is performed, and the feedforward PD control compensation measures are established for the joint friction. An experimental analysis is also conducted to verify the effect of the information fusion and error compensation on the accuracy of the OMPS.

scholarly journals Earthquake-hit Population Spatial Evaluation Based on Correlation Characteristics and BP Neural Network

2021 ◽  
Author(s):  
Hanxu Zhou ◽  
ailan che ◽  
Xianghua Shuai
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Jiuzhaigou Earthquake ◽  
Per Capita Gdp ◽  
Spatial Characteristics ◽  
Random Samples ◽  
Per Capita ◽  
Sample Points ◽  
Spatial Evaluation

Abstract Rapid spatial evaluation of earthquake-hit population after earthquake occurrence is required in the disaster emergency rescue management, due to its significant support for decreasing casualties and property losses. The correlation between earthquake-hit population and influencing factors are analyzed using the data from the 2013 Ms7.0 Lushan earthquake. Ten influencing factors including elevation, slope angle, population density, per capita GDP, distance to fault, distance to river, NDVI, PGA, PGV and distance to epicenter, are classified into environmental factors and seismic factors. The correlation analysis reveals characteristics that there is a nonlinear relationship between the earthquake-hit population and various factors, and per capita GDP and PGA factor have a stronger correlation with earthquake-hit population. Moreover, the spatial variability of influencing factors would affect the distribution of earthquake-hit population. The earthquake-hit population is evaluated using BP neural network with optimizing training samples based on the spatial characteristics of per capita GDP and PGA factors. Different number of sample points are generated in areas with different value intervals of influencing factors, instead of the random distribution of sample points. The minimum value of RMSE (Root Mean Square Error) from testing set is 18 people/km2, showing good accuracy in the spatial evaluation of earthquake-hit population. Meanwhile, the optimizing samples considering spatial characteristics could improve the convergence speed and generalization capability comparing to random samples. The trained network was generalized to the 2017 Ms7.0 Jiuzhaigou earthquake to verify the prediction accuracy. The mean absolute error of earthquake-hit population evaluation results in different counties under the Jiuzhaigou earthquake were 18357 people and 26121 people for optimizing samples and random samples, respectively. The evaluation results indicate that BP neural network considering the correlation characteristics of factors has the capability to evaluate the earthquake-hit population in space, providing more detailed information for emergency service and rescue operation.

scholarly journals Spatial Evaluation of Earthquake Disaster Based On Correlation Characteristics And BP Neural Network

2021 ◽  
Author(s):  
Hanxu Zhou ◽  
Ailan Che ◽  
Xianghua Shuai
Keyword(s):  
Neural Network ◽  
Influencing Factors ◽  
Bp Neural Network ◽  
Jiuzhaigou Earthquake ◽  
Per Capita Gdp ◽  
Earthquake Disaster ◽  
Spatial Characteristics ◽  
Per Capita ◽  
Sample Points ◽  
Spatial Evaluation

Abstract Rapid spatial evaluation of disaster after earthquake occurrence is required in the emergency rescue management, due to its significant support for decreasing casualties and property losses. The earthquake-hit population is taken as an example of earthquake disaster to construct the evaluation model using the data from the 2013 Ms7.0 Lushan earthquake. Ten influencing factors are classified into environmental factors and seismic factors. The correlation analysis reveals characteristics that there is a nonlinear relationship between the earthquake-hit population and various factors, and per capita GDP and PGA factor have a stronger correlation with earthquake-hit population. Moreover, the spatial variability of influencing factors would affect the distribution of earthquake-hit population. The earthquake-hit population is evaluated using BP neural network with optimizing training samples based on the spatial characteristics of per capita GDP and PGA factors. Different number of sample points are generated in areas with different value intervals of influencing factors, instead of the random distribution of sample points. The minimum value of RMSE (Root Mean Square Error) from testing set is 18 people/km2, showing good accuracy in the spatial evaluation of earthquake-hit population. Meanwhile, the optimizing samples considering spatial characteristics could improve the convergence speed and generalization capability comparing to random samples. The trained network was generalized to the 2017 Ms7.0 Jiuzhaigou earthquake to verify the prediction accuracy. The evaluation results indicate that BP neural network considering the correlation characteristics of factors has the capability to evaluate the seismic disaster information in space, providing more detailed information for emergency service and rescue operation.

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