scholarly journals Modelling of Deformation Resistance with Big Data and Its Application in the Prediction of Rolling Force of Thick Plate

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shun Hu Zhang ◽  
Li Zhi Che ◽  
Xin Ying Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Rolling Force ◽  
Deformation Resistance ◽  
Force Model ◽  
The Neural Network ◽  
Bp Neural Network Model

The precision of traditional deformation resistance model is limited, which leads to the inaccuracy of the existing rolling force model. In this paper, the back propagation (BP) neural network model was established according to the industrial big data to accurately predict the deformation resistance. Then, a new rolling force model was established by using the BP neural network model. During the establishment of the neural network model, the data set of deformation resistance was established, which was calculated back from the actual rolling force data. Based on the data set after normalization, the BP neural network model of deformation resistance was established through the optimization of algorithm and network structure. It is shown that both the prediction accuracy of the neural network model on the training set and the test set are high, indicating that the generalization ability of the model is strong. The neural network model of the deformation resistance is compared with the theoretical one, and the maximum error is only 3.96%. Furthermore, by comparison with the traditional rolling force model, it is found that the prediction accuracy of the rolling force model imbedding with the present neural network model is improved obviously. The maximum error of the present rolling force model is just 3.86%. The research in this paper provides a new way to improve the prediction accuracy of rolling force model.

scholarly journals An Inventory Controlled Supply Chain Model Based on Improved BP Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Wei He
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Convergence Rate ◽  
Inventory Control ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
Inventory Level ◽  
Bp Neural Network Model

Inventory control is a key factor for reducing supply chain cost and increasing customer satisfaction. However, prediction of inventory level is a challenging task for managers. As one of the widely used techniques for inventory control, standard BP neural network has such problems as low convergence rate and poor prediction accuracy. Aiming at these problems, a new fast convergent BP neural network model for predicting inventory level is developed in this paper. By adding an error offset, this paper deduces the new chain propagation rule and the new weight formula. This paper also applies the improved BP neural network model to predict the inventory level of an automotive parts company. The results show that the improved algorithm not only significantly exceeds the standard algorithm but also outperforms some other improved BP algorithms both on convergence rate and prediction accuracy.

Modelling of viscosity of fluorine-free mold fluxes using neural network

2019 ◽  
Vol 116 (2) ◽  
pp. 201
Author(s):  
Xiaoli Yuan ◽  
Lin Wang ◽  
Jianqiang Zhang ◽  
Oleg Ostrovski ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Viscosity Data ◽  
The Neural Network ◽  
Mold Fluxes ◽  
Experimental Values ◽  
Bp Neural Network Model

Viscosity is an important property of mold fluxes for steel continuous casting. However, direct measurement of viscosity of multi-component systems in a broad range of temperatures and compositions is an onerous work and has some limitations. This paper developed a model using the back propagation (BP) neural network to describe the viscosity of fluorine-free mold fluxes. The BP neural network model was developed and validated using 70 experimental values of viscosity of fluorine-free mold fluxes CaO-SiO2-Al2O3-B2O3-Na2O-TiO2-MgO-Li2O-MnO-ZrO2; 51 of them were used for developing the neural network model and the rest 19 viscosity data for the model validation. Calculated viscosities were in a good agreement with the experimental data. Based on the developed model, the effects of temperature and composition on the viscosity of fluorine-free fluxes were predicted and discussed.

scholarly journals Pricing 50ETF Option Based on Genetic Algorithm BP Model

2018 ◽  
Vol 227 ◽  
pp. 02010
Author(s):  
Yulin Du
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Theory And Practice ◽  
The Neural Network ◽  
Finance Theory ◽  
S Model ◽  
Bp Neural Network Model ◽  
Bp Model

Pricing financial derivatives is focus in finance theory and practice. Comparing to the traditional parameter model pricing method, the neural network method has obvious advantages in solving this problem. In this paper,we will price the option of Shanghai 50ETF based on the improved BP neural network model (GABP). The results show that the effect of neural network is better than that of B-S model, and the accuracy of GABP model is higher than that of BP neural network model and B-S model.

Research on Forecasting of Airflow Temperature in Heading Face Based on the GA-BP Neural Network

2012 ◽  
Vol 524-527 ◽  
pp. 668-672
Author(s):  
Xiang Zhang ◽  
Bai Shun Wang ◽  
Shuo Xu
Keyword(s):  
Neural Network ◽  
Genetic Algorithms ◽  
Network Model ◽  
Neural Network Model ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Prediction Accuracy ◽  
High Prediction ◽  
Bp Neural Network Model ◽  
Matlab Programming

In order to solve the problem of forecasting airflow temperature in heading face, a new model of forecasting airflow temperature in heading face with Matlab programming is built on the BP neural network model, making use of genetic algorithms to optimize the initial weights and thresholds of the network. According to the analysis of test carried out in a coal mine in Huainan, the results show that the model of fast convergence and high prediction accuracy is one of the most effective ways of forecasting airflow temperature in heading face.

Modeling and Analysis of Plunge Milling Force Based on Orthogonal Experiment

2013 ◽  
Vol 391 ◽  
pp. 372-375 ◽  
Author(s):  
Xing Hua Niu ◽  
Xian Li Meng ◽  
Zhen Tao Zhang ◽  
Rui Zhao ◽  
Bo Fei Shen
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Empirical Formula ◽  
Orthogonal Experiment ◽  
Force Model ◽  
Plunge Milling ◽  
Milling Force ◽  
Bp Neural Network Model

Plunge milling force experiment was designed based on the method of orthogonal experiment, selecting Cr12 mold steel as the experimental material for obtaining the measurement data. Combined with the experimental data, the empirical formula of the milling force model and BP neural network model were established respectively. The two types model are analyzed and compared. The results show that the BP neural network model has a better prediction effect than traditional empirical formula.

Based on the CAM Error Prediction Research of BP Neural Network

2012 ◽  
Vol 472-475 ◽  
pp. 437-441
Author(s):  
Guan Wei Jia ◽  
Qiu Shi Han ◽  
Qi Guang Li ◽  
Bao Ying Peng
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Numerical Control ◽  
Error Prediction ◽  
Actual Process ◽  
The Neural Network ◽  
Bp Neural Network Model ◽  
Siemens 840D

The double channel function of Siemens 840d numerical control system collected the input data needed by BP neural network model. Combined with detection lift error in actual process of machining CAM, the detecting lift error is predicted with using of BP neural network model designed by the neural network toolbox and the train of neural network. The test results of this method are proved to achieve the predicted effect, which means that can be used in the CAM processing lift error prediction.

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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