Predicition of GRT Fiber-Rubberized Haydite Concrete Compressive Strength Based on Multiple Regreeion Analysis and BP Neural Network

2012 ◽  
Vol 472-475 ◽  
pp. 60-65
Author(s):  
Bing Hua Xia ◽  
Yuan Cai Liu ◽  
De Bin Zhu
Keyword(s):  
Neural Network ◽  
Compressive Strength ◽  
Multiple Regression ◽  
Test Data ◽  
Bp Neural Network ◽  
Intensity Level ◽  
Regression Equations ◽  
Strength Based ◽  
Neural Network Theory ◽  
Bp Neural Network Model

Experiment with intensity level for the LC30 ceramsite concrete as the research object, changing the content of cement, GRT fiber, rubber powder by the orthogonal test to configure GRT fiber—rubberized haydite concrete samples, maintenance samples 7d and 28d in standard conditions and respectively testing their standard compressive strength. Through the analysis of the test data, using multiple regression analysis established the GRT fiber—rubberized haydite concrete 7d and 28d standard compressive strength regression formulas.By means of BP neural network theory combine MATLAB programme established GRT fiber—rubberized haydite concrete 7d and 28d standard compressive strength neural network model.Finally using 3 groups new test data to compare the value of multiple regression equations and BP neural network’s predicted value.The results indicate that the multiple regression equations and BP neural network model are availabled.

Predication of GRT Fiber-Rubberized Haydite Concrete Bend Strength Based on Multiple Regression Analysis and BP Neural Network

2012 ◽  
Vol 174-177 ◽  
pp. 1100-1106
Author(s):  
Bing Hua Xia ◽  
Yuan Cai Liu ◽  
Wei Wei Sun
Keyword(s):  
Neural Network ◽  
Regression Analysis ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Test Data ◽  
Bp Neural Network ◽  
Hydration Reaction ◽  
Intensity Level ◽  
Regression Equations ◽  
Bend Strength

Experiment with intensity level for the LC30 ceramsite concrete as the research object, changing the content of cement, GRT fiber, rubber powder by the orthogonal test to configure GRT fiber—rubberized haydite concrete samples, maintenance samples 7d and 28d in standard conditions and respectively testing their bend strength. Through the analysis of the test data, using multiple regression analysis established the GRT fiber—rubberized haydite concrete 7d and 28d bend strength regression formulas.By means of BP neural network theory combine MATLAB programme established GRT fiber—rubberized haydite concrete 7d and 28d bend strength neural network model.Finally using 3 groups new test data to compare the value of multiple regression equations and BP neural network’s predicted value.The results indicate that the multiple regression equations of 28d’s and 28d’s BP neural network model are availabled.But because of the water and cement which in the GRT fiber—rubberized haydite concrete can not hydration reaction sufficiently during the 7d’s,so the multiple regression equations of 7d’s is unavailabled.

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.

Analysis of pressure distribution during movement for the top part of female socks

2019 ◽  
Vol 90 (11-12) ◽  
pp. 1301-1310
Author(s):  
Xiang-hui Yan ◽  
Li-jun Wang ◽  
Ming-ling Wang ◽  
Jia-ling Shi
Keyword(s):  
Neural Network ◽  
Pressure Distribution ◽  
Multiple Regression ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Dynamic Pressure ◽  
Movement Velocity ◽  
Transverse Tension ◽  
Bp Neural Network Model

Female sports socks were studied to achieve the correlation between the ankle surface curvature and pressure distribution of the top part of socks. The transverse tension performance of the socks’ top part was obtained using an Instron universal strength tester, and the leg size was measured with a [TC]2 contactless 3D body scanner. The pressure was monitored by a Pliance-X-32 pressure test system. Gray correlation, variance, and regression analysis were applied to study the correlation between movement velocity, fabric performance, leg circumference, and ankle pressure distribution. The dynamic pressure prediction models of multiple regression and back propagation (BP) neural network on the top part of socks were also established. The results show that the transverse tension performance and sock density have a significant effect on the ankle static pressure. Movement velocity, sock density, and leg circumference are positively correlated with dynamic pressure, while the elastic recovery rate of the fabric is negatively correlated with the pressure. Both of the multiple regression and BP neural network models can predict the dynamic pressure, and the BP neural network model is better than multiple regression at prediction error, which was kept to less than 0.5%. Therefore, the BP neural network model can be effectively used in female ribbed sock top design.

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.

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.

scholarly journals Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

2020 ◽  
Vol 12 (12) ◽  
pp. 168781402098468
Author(s):  
Xianbin Du ◽  
Youqun Zhao ◽  
Yijiang Ma ◽  
Hongxun Fu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Adaptive Learning ◽  
High Speed ◽  
Back Propagation ◽  
Lateral Force ◽  
Learning Ability ◽  
Vehicle Performance ◽  
Neural Network Theory ◽  
Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

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