An Influence Factors of Ozone Pollution Based on BP Neural Network

2022 ◽  
pp. 1305-1313
Author(s):  
Hao Zheng ◽  
Yanfen Gao ◽  
Huifeng Xue ◽  
Shan Gao ◽  
Feng Zhang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Influence Factors ◽  
Ozone Pollution

Research on Applied-Information Technology with PM2.5 Generation and Evolution Model Based on BP Neural Network

2014 ◽  
Vol 1003 ◽  
pp. 226-229 ◽  
Author(s):  
Ying Hong Xie ◽  
Xiao Wei Han ◽  
Qi Li
Keyword(s):  
Neural Network ◽  
Information Technology ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Influence Factors ◽  
Evolution Model ◽  
Good Reliability ◽  
Bp Neural Network Model ◽  
Near Future ◽  
Xi’An City

In this paper, BP neural network model is used to establish the occurrence and evolution model of PM2.5 in an area in Xi'an city. In the model, wind, humidity, season, SO2,NO2,PM10, CO,O3 (in one hour ) and O3 (in eight hours ) and other influence factors are all considered. The model has good reliability, it can accurately forecast the value of PM2.5 and its variation in the near future, which can provide the basis for the PM2.5 control.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

BP Neural Network Model on Choice of Project Manager for Highway Slope Treatment

2014 ◽  
Vol 505-506 ◽  
pp. 274-277
Author(s):  
Bin Wang ◽  
Yong Tao Gao
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Theory ◽  
Influence Factors ◽  
Project Manager ◽  
Slope Treatment ◽  
Neural Network Theory ◽  
Bp Neural Network Model

To get the quantified indexes of comprehensive capacity about project manager, based on the modal on artificial neural network theory, different influence factors about choice of project manager for highway slope treatment were analyzed , identified, quantified and evaluated , then comprehensive capacity of the manager were analyzed. Such procedure provided a new method for choice of project manager for highway slope treatment.

The Analysis of Demand Characteristics of Passenger Transportation Based on BP Neural Network

2013 ◽  
Vol 409-410 ◽  
pp. 1292-1295 ◽  
Author(s):  
Yong Liao ◽  
Tao Wu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Stated Preference ◽  
Orthogonal Design ◽  
Influence Factors ◽  
Demand Characteristic ◽  
Survey Method ◽  
Demand Characteristics ◽  
Transport Mode ◽  
Passenger Transportation

To quantitatively analyze travelers preference under different influence factors on transport mode, the thoughts of orthogonal design is introduced into optimizing questionnaires without the loss of information. Combined with stated preference survey method, data related to travelers preference are collected. In order to analyze survey data scientifically and roundly, BP neural network is used. By taking non-investigated options as inputs, BP neural network can figure out the scores of all options which are not be surveyed. At last a numeric example demonstrates the use of BP neural network in the analysis of demand characteristic of passenger transposition is valid and feasible.

Applied-Information Technology in Power System with Short-Term Load Forecasting Based on SPSS and BP Neural Network

2014 ◽  
Vol 1022 ◽  
pp. 241-244 ◽  
Author(s):  
Jian Ping Chen ◽  
Chang Hao Xia ◽  
Zhi Peng Tian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Predictive Accuracy ◽  
Load Forecasting ◽  
Influence Factors ◽  
Principal Component ◽  
Weather Data ◽  
Power Load ◽  
Bp Neural Network Model ◽  
Bp Model

In the study of power load forecasting, the factors influencing power load have data redundancy and data nonlinearity. The traditional load forecasting methods can’t eliminate redundant or nonlinear law between data, which result in reduced accuracy. In order to improve the predictive accuracy of power load, a prediction model based on BP neural network and SPSS (SPSS-BP) is established. The paper first analyzes the correlation and principal component of influence factors of electric power load, which eliminates the redundancy between various factors, accelerates the speed of BP neural network forecasting and improves predictive accuracy; then model the processed data and forecast through the BP neural network model. One-month weather data and load data of Yichang city have been confirmatory tested and analyzed through application of SPSS-BP model. The results show that SPSS-BP model significantly improves the accuracy, verify the feasibility and effectiveness of the model.

Artificial Neural Network Analysis Method of Concrete Performance Based on Virtual Data Samples

2010 ◽  
Vol 34-35 ◽  
pp. 462-466
Author(s):  
Jun Wei Song ◽  
Yan Shi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Bp Neural Network ◽  
Influence Factors ◽  
Forecast Performance ◽  
Virtual Samples ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural ◽  
Predicted Values ◽  
The Relationship

The relationship between concrete performance and influence factors is uncertain and nonlinear. Accordingly, present BP neural network and virtual samples are presented to predict concrete performance in this paper. At first neural network and matters which need attention are introduced, And frost resistance forecasting model and impermeability model are built up, which are three-tier BP neural network of 6-13-2,4-9-1.The results show that the predicted values are ideal, and artificial neural network as one of the methods to forecast performance of concrete is appropriate.

Bone Scaffold Forming Filament Width Prediction of LDM Based on the Improved BP Neural Network

2013 ◽  
Vol 568 ◽  
pp. 187-192
Author(s):  
Yuan Yuan Liu ◽  
Zhen Zhong Han ◽  
Shu Hui Fang ◽  
Da Li Liu ◽  
Ying Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Pore Size ◽  
Bp Neural Network ◽  
Three Dimensional ◽  
Influence Factors ◽  
Bone Scaffold ◽  
Forming Process ◽  
Size Accuracy ◽  
Forming Precision ◽  
Neural Network Theory

LDM process is used for preparing three-dimensional scaffolds for tissue engineering rapid prototyping technologies. Because of its forming process is complex, which influenced by a variety of factors, so the processing environment is not stable, the forming of scaffold pore size can not be guaranteed, therefore the forming precision is poor. However, the scaffold pore size accuracy is mainly decided by the wire filament width. Neural network theory and development provides a powerful tool for the study of nonlinear systems. This article analyzed the influence factors for forming bone scaffold filament width of LDM process, based on improved BP neural network, using MATLAB software programming, then predicted the filament width. The results show that model prediction error was less than 8%, it has high forecasting precision, and it can be used to guide the LDM process parameter selection and forming precision of prediction.

Sensitivity Analysis of Slope Stability Influence Factors Based on BP Neural Network

2014 ◽  
Vol 1010-1012 ◽  
pp. 1544-1547 ◽  
Author(s):  
Chuang Ye Wang ◽  
Fei Zhang ◽  
Wan Dong Han
Keyword(s):  
Neural Network ◽  
Slope Stability ◽  
Bp Neural Network ◽  
Influence Factor ◽  
Pressure Coefficient ◽  
Slope Angle ◽  
Influence Factors ◽  
Friction Angle ◽  
Analysis Model ◽  
Rock Bulk

There are many factors which influence the slope stability. In order to analyze the degree of importance of each influence factor on slope stability, this paper establishes a slope stability analysis model based on BP neural network. The computation results showed that the model was reasonable and reliable. On this basis, the sensitivity of various influence factors to slope stability was analyzed by single-factor test, which were internal friction angle of rock, bulk density, pore pressure coefficient, slope angle, rock cohesion and slope height in a descending order of sensitivity.

Prediction of Wear Volumes to 45 Steel of NBR Based on Neural Network

2011 ◽  
Vol 66-68 ◽  
pp. 788-792
Author(s):  
Xuan Luo ◽  
Shi Jie Wang ◽  
Xiao Ren Lv
Keyword(s):  
Neural Network ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Rotational Speed ◽  
Friction And Wear ◽  
Influence Factors ◽  
Wear Volume ◽  
Experimental Parameters ◽  
Bp Neural Network Model ◽  
Main Influence

The wear to orthogonal metals of NBR is the main cause of affecting the endurance of ESPCP. The rotational speed, load and temperature are main influence factors of the wear of 45 steel. The BP neural network model used in the forecast of the 45 steel wear volume was established. The 45 steel wear volume was obtained using friction and wear machine under different experimental parameters. The wear volumes of different experimental parameters were forecasted using BP neural network. The results indicate that it is feasible to forecast the rotational speed, load and temperature to 45 steel wear volume.

Research on the Embedded Software Reliability Evaluation Model Based on Influence Factors

2013 ◽  
Vol 397-400 ◽  
pp. 2355-2359
Author(s):  
Pei Tian ◽  
Li Na Gong ◽  
Wei Li ◽  
Wen Tao Tian
Keyword(s):  
Neural Network ◽  
Software Reliability ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Influence Factors ◽  
Embedded Software ◽  
Reliability Evaluation ◽  
Model Based ◽  
Preliminary Application

This paper firstly analyzes the reliability Influence factors of the embedded software, then puts forward an embedded software reliability evaluation model based on BP neural network, finally the preliminary application and verification of the model is given.

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