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.

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 of Developing Modern Agriculture Demands for the Agricultural Scientific Research Institutions Services Based on BP Artificial Neural Network

2019 ◽  
Vol 131 ◽  
pp. 01122
Author(s):  
Hongyan Sun ◽  
Hongwen Bi ◽  
Jingyuan Wang ◽  
Yu Zhang ◽  
Yanqi Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Scientific Research ◽  
Research Institutions ◽  
Bp Artificial Neural Network ◽  
Modern Agriculture ◽  
Bp Neural Network Model

BP artificial neural network model is used to predict developing modern agriculture demands for the agricultural scientific research institutions services. Starting from the brief introduction of the usages of BP neural network, we analyzed the demand factors of the agricultural scientific research institutions services and the affective elements of the demands, use the BP neural network model to predict, and then run the BP neural network model on the MATLAB platform, and finally carry out the case studies of Heilongjiang Province.

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.

scholarly journals Predicting postoperative pain following root canal treatment by using artificial neural network evaluation

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xin Gao ◽  
Xing Xin ◽  
Zhi Li ◽  
Wei Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Postoperative Pain ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Root Canal ◽  
Root Canal Treatment ◽  
Artificial Neural ◽  
Bp Neural Network Model

AbstractThis study aimed to evaluate the accuracy of back propagation (BP) artificial neural network model for predicting postoperative pain following root canal treatment (RCT). The BP neural network model was developed using MATLAB 7.0 neural network toolbox, and the functional projective relationship was established between the 13 parameters (including the personal, inflammatory reaction, operative procedure factors) and postoperative pain of the patient after RCT. This neural network model was trained and tested based on data from 300 patients who underwent RCT. Among these cases, 210, 45 and 45 were allocated as the training, data validation and test samples, respectively, to assess the accuracy of prediction. In this present study, the accuracy of this BP neural network model was 95.60% for the prediction of postoperative pain following RCT. To conclude, the BP network model could be used to predict postoperative pain following RCT and showed clinical feasibility and application value.

Application of BP Neural Network to Tendency Prediction of State Monitoring Data

2014 ◽  
Vol 556-562 ◽  
pp. 2744-2747 ◽  
Author(s):  
Xu De Cheng ◽  
Hong Li Wang ◽  
Bing Xu ◽  
Wei Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Theory ◽  
Monitoring Data ◽  
Data Sets ◽  
State Monitoring ◽  
Multiple Data ◽  
Multiple Data Sets ◽  
Neural Network Theory ◽  
Bp Neural Network Model

BP neural network model for state monitoring data tendency prediction is constructed based upon neural network theory, and simulation programming is achieved with MATLAB. In the experiment, multiple data sets are selected for training and testing of the network to prove the validity of algorithm and model.

Forecast of Blasting Fragmentation Distribution Based on BP Neural Network

2012 ◽  
Vol 619 ◽  
pp. 3-8 ◽  
Author(s):  
Hong Gao ◽  
Zhi Liang Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Industrial Production ◽  
Influence Factors ◽  
Model System ◽  
Rock Blasting ◽  
Bp Neural Network Model ◽  
Main Influence

The influence factors of rock blasting fragmentation distribution are analyzed, 10 main influence factors are selected from these. Using the MATLAB BP neural network model system to analyze blasting fragmentation distribution of +860 level face at Yi Chun tantalum-niobium mine, successfully predicted the blasting fragmentation distribution, it is favorable to design the blasting parameters and industrial production.

Estimating nitrogen contents of apple leaves based on hyperspectral parameter models at different phenophases

2016 ◽  
Vol 6 (2) ◽  
pp. 942-952
Author(s):  
Xicun ZHU ◽  
Zhuoyuan WANG ◽  
Lulu GAO ◽  
Gengxing ZHAO ◽  
Ling WANG
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Shoot Growth ◽  
Hyperspectral Data ◽  
Estimation Model ◽  
Apple Leaves ◽  
Bp Neural Network Model ◽  
Nitrogen Contents

The objective of the paper is to explore the best phenophase for estimating the nitrogen contents of apple leaves, to establish the best estimation model of the hyperspectral data at different phenophases. It is to improve the apple trees precise fertilization and production management. The experiments were done in 20 orchards in the field, measured hyperspectral data and nitrogen contents of apple leaves at three phenophases in two years, which were shoot growth phenophase, spring shoots pause growth phenophase, autumn shoots pause growth phenophase. The study analyzed the nitrogen contents of apple leaves with its original spectral and first derivative, screened sensitive wavelengths of each phenophase. The hyperspectral parameters were built with the sensitive wavelengths. Multiple stepwise regressions, partial least squares and BP neural network model were adopted in the study. The results showed that 551 nm, 716 nm, 530 nm, 703 nm; 543 nm, 705 nm, 699 nm, 756 nm and 545 nm, 702 nm, 695 nm, 746 nm were sensitive wavelengths of three phenophases. R551+R716, R551*R716, FDR530+FDR703, FDR530*FDR703; R543+R705, R543*R705, FDR699+FDR756, FDR699*FDR756and R545+R702, R545*R702, FDR695+FDR746, FDR695*FDR746 were the best hyperspectral parameters of each phenophase. Of all the estimation models, the estimated effect of shoot growth phenophase was better than other two phenophases, so shoot growth phenophase was the best phenophase to estimate the nitrogen contents of apple leaves based on hyperspectral models. In the three models, the 4-3-1 BP neural network model of shoot growth phenophase was the best estimation model. The R2 of estimated value and measured value was 0.6307, RE% was 23.37, RMSE was 0.6274.

scholarly journals Regional logistics demand forecasting: a BP neural network approach

2021 ◽  
Author(s):  
Lijuan Huang ◽  
Guojie Xie ◽  
Wende Zhao ◽  
Yan Gu ◽  
Yi Huang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Demand Forecasting ◽  
Indicator System ◽  
Regional Logistics ◽  
Logistics Enterprises ◽  
Logistics Demand ◽  
Bp Neural Network Model

AbstractWith the rapid development of e-commerce, the backlog of distribution orders, insufficient logistics capacity and other issues are becoming more and more serious. It is very significant for e-commerce platforms and logistics enterprises to clarify the demand of logistics. To meet this need, a forecasting indicator system of Guangdong logistics demand was constructed from the perspective of e-commerce. The GM (1, 1) model and Back Propagation (BP) neural network model were used to simulate and forecast the logistics demand of Guangdong province from 2000 to 2019. The results show that the Guangdong logistics demand forecasting indicator system has good applicability. Compared with the GM (1, 1) model, the BP neural network model has smaller prediction error and more stable prediction results. Based on the results of the study, it is the recommendation of the authors that e-commerce platforms and logistics enterprises should pay attention to the prediction of regional logistics demand, choose scientific forecasting methods, and encourage the implementation of new distribution modes.

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