A new prediction model of CO2 diffusion coefficient in crude oil under reservoir conditions based on BP neural network

On the basis of orthogonal test analysis of variance, BP neural network is used to forecast quantitatively the stamping spring-back of front panel of a car body, namely the engine hood, under the conditions of different stamping parameters. Firstly, BP neural network prediction model is established and sample training is done in Matlab. Then, the spring-back prediction using BP neural network and the result of spring-back simulation using Dynaform is compared to verify the precision and stability of the prediction model. Lastly, modification is made to the BP neural network according to practical stamping parameters and an efficient BP neural network model is established. Using this model, stamping spring-back prediction for the front panel of a car body is made. The spring-back prediction could then be used for spring-back compensation in the mould design of the front panel.

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Prediction of Sand Thickness Using Seismic Attributes Based on BP-Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.524-527.180 ◽

2012 ◽

Vol 524-527 ◽

pp. 180-183

Author(s):

Feng Gao

Keyword(s):

Neural Network ◽

Prediction Model ◽

Bp Neural Network ◽

Seismic Attributes ◽

Bp Network ◽

Sand Body ◽

Precise Prediction ◽

Energy Maximum ◽

Bp Model ◽

Rms Amplitude

Total energy, maximum peak amplitude and RMS amplitude are sensitive to sand body, and they are non-linear relations with sand thickness. In this study, a three-layer BP neural network is employed to build the prediction model. Nine samples were analyzed by three-layer BP network. The relationships were produced by BP network between sand thickness and the three seismic attributes. The precise prediction results indicate that the three-layer BP network based modeling is a practically very useful tool in prediction sand thickness. The BP model provided better accuracy in prediction than other methods.

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Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

Computational Intelligence and Neuroscience ◽

10.1155/2021/1026978 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Shaobo Lu

Keyword(s):

Neural Network ◽

Time Series ◽

Prediction Model ◽

Error Correction ◽

Relative Error ◽

Bp Neural Network ◽

Arima Model ◽

Forecast Model ◽

Price Prediction ◽

The Neural Network

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

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Prediction of the Deformation of Aluminum Alloy Drill Pipes in Thermal Assembly Based on a BP Neural Network

Applied Sciences ◽

10.3390/app12020757 ◽

2022 ◽

Vol 12 (2) ◽

pp. 757

Author(s):

Xiaofeng Wang ◽

Baochang Liu ◽

Jiaqi Yun ◽

Xueqi Wang ◽

Haoliang Bai

Keyword(s):

Neural Network ◽

Aluminum Alloy ◽

Prediction Model ◽

Bp Neural Network ◽

Prediction Accuracy ◽

Thermal Deformation ◽

Heating Temperature ◽

Cooling Water ◽

Interference Fit ◽

Steel Joint

The connection between the steel joint and aluminum alloy pipe is the weak part of the aluminum alloy drill pipe. Practically, the interference connection between the aluminum alloy rod and the steel joint is usually realized by thermal assembly. In this paper, the relationship between the cooling water flow rate, initial heating temperature and the thermal deformation of the steel joint in interference thermal assembly was studied and predicted. Firstly, the temperature data of each measuring point of the steel joint were obtained by a thermal assembly experiment. Based on the theory of thermoelasticity, the analytical solution of the thermal deformation of the steel joint was studied. The temperature function was fitted by the least square method, and the calculated value of radial thermal deformation of the section was finally obtained. Based on the BP neural network algorithm, the thermal deformation of steel joint section was predicted. Besides, a prediction model was established, which was about the relationship between cooling water flow rate, initial heating temperature and interference. The magnitude of interference fit of steel joint was predicted. The magnitude of the interference fit of the steel joint was predicted. A polynomial model, exponential model and Gaussian model were adopted to predict the sectional deformation so as to compare and analyze the predictive performance of a BP neural network, among which the polynomial model was used to predict the magnitude of the interference fit. Through a comparative analysis of the fitting residual (RE) and sum of squares of the error (SSE), it can be known that a BP neural network has good prediction accuracy. The predicted results showed that the error of the prediction model increases with the increase of the heating temperature in the prediction model of the steel node interference and related factors. When the cooling water velocity hit 0.038 m/s, the prediction accuracy was the highest. The prediction error increases with the increase or decrease of the velocity. Especially when the velocity increases, the trend of error increasing became more obvious. The analysis shows that this method has better prediction accuracy.

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The Establishment of Sorts Performance Prediction Model Based on BP Neural Network and Solution by SPSS

Proceedings of the 2nd International Conference on Management Science and Industrial Engineering (MSIE 2013) ◽

10.2991/msie-13.2013.19 ◽

2013 ◽

Author(s):

Xiaomei Qi

Keyword(s):

Neural Network ◽

Prediction Model ◽

Performance Prediction ◽

Bp Neural Network ◽

Model Based

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Railway Freight Turnover Forecast Based on the BP Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.536-537.837 ◽

2014 ◽

Vol 536-537 ◽

pp. 837-840

Author(s):

Jiang Sun ◽

Chong Wei

Keyword(s):

Neural Network ◽

Prediction Model ◽

Network Model ◽

Neural Network Model ◽

Bp Neural Network ◽

Railway Freight ◽

Bp Neural Network Model ◽

Trained Network

A BP neural network model was employed to forecast the railway freight turnover. First, this paper analyses the data of railway freight turnover in China from 1998 to 2012, build a three layers BP neural network, then by training and learning, a well-trained network can be used for simulating and forecasting. Finally, predict by the Grey GM(1,1) model and well-trained BP neural network respectively, and compares the errors of two prediction model, the results show that predicting the railway freight turnover by BP neural network has higher precision.

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