Research on Treatment of Retaining Wall Foundation with Geosynthetics Based on BP Neural Network

Through the long-term load creep test of CE131 geonet and SD L25 retaining wall foundation, which are widely used in reinforced earth engineering, a large number of experimental data are obtained. On this basis, the least-squares and BP neural network are used to predict its creep variables. The principle of least squares is to find a curve in the curve family to fit the experimental data. From the sum of the squared errors σ = 0. 001 16, the fitting accuracy is higher. The BP neural network has adaptive learning and memory capabilities, especially the three-layer BP neural network model. The maximum error between the predicted value and the actual value is 0.91%, which is a lot better than the error of the least square 3.4%. This method Found a new way for creep prediction.

Health Evaluation of Larix Kaempferi Plantation Ecosystem Based on BP Neural Network Model

10.4028/www.scientific.net/amr.663.823 ◽

2013 ◽

Vol 663 ◽

pp. 823-826

Author(s):

Shu Rong Hui ◽

Xiao Xiao Dai ◽

Hui Liu ◽

Qiang Liu

Keyword(s):

Neural Network ◽

Forest Ecosystem ◽

Adaptive Learning ◽

Bp Neural Network ◽

Ecosystem Health ◽

Tree Height ◽

Slope Aspect ◽

Larix Kaempferi ◽

The Index System ◽

Based on the basic theory of the forest ecosystem, we build the index system to evaluate forest ecosystem health from the stand-scale and take advantage of improved BP neural network to evaluate the ecosystem health of Larix Kaempferi plantation in the Liaodong area quantitatively. And then we analyze the stand-scale health grade status according to different slope aspect, forest age, average tree height and altitude. The results indicate that we make the satisfactory process to research the complex forest ecosystem using the improved BP neural network. The improved BP neural network which uses the momentum-adaptive learning rate adjustment algorithm and L-M learning rules decreases iteration times, makes the convergence speed very fast and improve the precision.

Research of GPS Elevation Conversion Based on Least Square Support Vector Machine and BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.501-504.2166 ◽

2014 ◽

Vol 501-504 ◽

pp. 2166-2171 ◽

Cited By ~ 2

Author(s):

Li Long Liu ◽

Teng Xu Zhang ◽

Miao Zhou ◽

Wei Wang ◽

Liang Ke Huang

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Bp Neural Network ◽

Curve Surface ◽

Least Square ◽

Support Vector ◽

Combined Model ◽

Polynomial Curve ◽

Optimal Weighting ◽

This paper proposed the optical weighting combined mode of Least Square Support Vector Machine (LS-SVM) and BP Neural network. According to the measured data, this paper compared and analyzed the accuracy of LS-SVM model, BP Neural network model; quadratic polynomial curve surface fitting based on Total least-square algorithm and optimal weighting combined model, the data shows that the optimal weighting combined model has higher precision then others.

Prediction of TC16 Alloy Deformation Behavior Based on BP Neural Network

10.4028/www.scientific.net/amr.850-851.96 ◽

2013 ◽

Vol 850-851 ◽

pp. 96-101

Author(s):

Chuan Dong Song ◽

Jian Kong ◽

Lin Che

Keyword(s):

Neural Network ◽

Experimental Data ◽

Mechanical Properties ◽

Neural Network Model ◽

Bp Neural Network ◽

Deformation Behavior ◽

Prediction Errors ◽

Quenching Temperature ◽

Network Method ◽

Based on the theory and algorithm of BP neural network, the deformation behavior of TC16 titanium alloys is studied under different quenching temperature. Mechanical properties of TC16 were measured by tensile experiments and use the BP neural network model to train and simulate the experimental data. Results show that using the BP neural network method can get high calculation accuracy and the prediction errors are around 5%.This method is suitable for further research of TC16 alloy.

Experimental Study on Calibration Model Based on Pt100 Temperature Sensor

10.4028/www.scientific.net/amr.798-799.402 ◽

2013 ◽

Vol 798-799 ◽

pp. 402-406

Author(s):

Peng Fei Li ◽

Cheng Yv ◽

Yong Ping Yang

Keyword(s):

Neural Network ◽

Experimental Data ◽

Bp Neural Network ◽

Temperature Sensor ◽

Least Square Method ◽

Least Square ◽

Calibration Model ◽

Calibration Experiment ◽

The Relationship ◽

Lm Algorithm

In order to improve measuring-temperature accuracy of the PT100 temperature sensor, we conduct multi-point calibration experiment. The BP neural network based on LM algorithm can process experimental data and the least square method can fit out more accurate formula that express the relationship between the temperature and resistance. It is available that this arithmetic that the interrelated experiment demonstrate its accuracy improve precision of the PT100 temperature sensor.This arithmetic can be applied to the calibration test.

BP Neural Network Based Optimization for China Railway Freight Transport Network

10.4028/www.scientific.net/amr.1037.404 ◽

2014 ◽

Vol 1037 ◽

pp. 404-410 ◽

Cited By ~ 1

Author(s):

Yan Sun ◽

Mao Xiang Lang ◽

Dan Zhu Wang

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Adaptive Learning ◽

Bp Neural Network ◽

Freight Transport ◽

Transport Network ◽

Railway Freight ◽

Bp Neural Network Model ◽

Railway Freight Transport

In order to optimize the railway freight transport network, integrate the limited transport resources and overcome the current problems existing in the traditional transport organization, in this study, we propose a three-layer railway freight transport network system, analyze its hierarchical structure and describe the respective function orientation of the railway freight stations in different layers. Then we design a BP neural network model with adaptive learning algorithm and momentum BP algorithm to classify the railway freight stations into three layers. Finally, an empirical case study is presented to test the feasibility of the BP neural network. The simulation result indicates that the BP neural network model can classify the railway freight stations into three layers under relatively high training accuracy.

An Application of BP Neural Network Model to Predict the Moisture Content of Crude Oil

10.4028/www.scientific.net/amr.524-527.1327 ◽

2012 ◽

Vol 524-527 ◽

pp. 1327-1330 ◽

Cited By ~ 1

Author(s):

Ying Ming Zhou ◽

Shu Wei Wang ◽

Lin Lin

Keyword(s):

Neural Network ◽

Experimental Data ◽

Moisture Content ◽

Crude Oil ◽

Heavy Oil ◽

Bp Neural Network ◽

Prediction Error ◽

Neural Network Prediction ◽

Network Prediction ◽

With the constant expansion of super heavy oil SAGD conversion development, the accurate testing of the crude oil in the high moisture content range is particularly important. In this paper, against the characteristics of Adopting SAGD technology exploiting heavy oil, BP neural network prediction model and calculation method has been adopted to predict the moisture content of crude oil. Through the study, the experimental data of the model were verified by the maximum prediction error is less than 3%, the accuracy of the forecast moisture content of crude oil to meet the site requirements. Through this study, the experimental data to the model was validated by the maximum prediction error is less than3%, the prediction accuracy of which to moisture content of crude oil is able to meet the requirements of the project site.

Study on Deformation Property of Soft Soil Based on Neural Networks

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.353-356.270 ◽

2013 ◽

Vol 353-356 ◽

pp. 270-273

Author(s):

Yong Jian Liu ◽

Zhang Ming Li ◽

Yin Wang ◽

Yi Mei Liu ◽

Yong Jian Chen ◽

...

Keyword(s):

Neural Network ◽

Network Model ◽

Neural Network Model ◽

Adaptive Learning ◽

Bp Neural Network ◽

Deformation Property ◽

Soft Soil ◽

Nonlinear Creep ◽

Propagation Network ◽

Based on the triaxial test results of soft soils, an error back propagation network predicting model for deformation property of soft soil is built. Improved BP neural network model is trained by additional momentum term, adaptive learning rate and Bayesian regularization performance function. Research shows that improved BP neural network model applied to predict soft soil foundation settlement, has fast computation, high accuracy, strong generalization ability, and good capability of matching the real data and the measured one. According to test data, the creep models can avoid any artificial assumption of complex constitutive equation, and can reflect nonlinear creep properties of soft soil objectively, thus has better fault-tolerance and more convenient than the traditional method.

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

JOURNAL OF ADVANCES IN AGRICULTURE ◽

10.24297/jaa.v6i2.5377 ◽

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.