scholarly journals Prediction of Grain Yield in Henan Province Based on Grey BP Neural Network Model

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bingjun Li ◽  
Yifan Zhang ◽  
Shuhua Zhang ◽  
Wenyan Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Henan Province ◽  
Grain Production ◽  
Relational Analysis ◽  
Key Variables ◽  
Input Variables ◽  
Bp Neural Network Model ◽  
Grey Relational ◽  
Bpnn Model

BP neural network (BPNN) is widely used due to its good generalization and robustness, but the model has the defect that it cannot automatically optimize the input variables. In response to this problem, this study uses the grey relational analysis method to rank the importance of input variables, obtains the key variables and the best BPNN model structure through multiple training and learning for the BPNN models, and proposes a variable optimization selection algorithm combining grey relational analysis and BP neural network. The predicted values from the metabolic GM (1, 1) model for key variables was used as input to the best BPNN model for prediction modeling, and a grey BP neural network model prediction model (GR-BPNN) was proposed. The long short-term memory neural network (LSTM), convolutional neural network (CNN), traditional BP neural network (BP), GM (1, N) model, and stepwise regression (SR) are also implemented as benchmark models to prove the superiority and applicability of the new model. Finally, the GR-BPNN forecasting model was applied to the grain yield forecast of the whole province and subregions for Henan Province. The forecasting results found that the growth rate of grain production in Henan Province slowed down and the center of gravity for grain production shifted northwards.

Sunlight Greenhouse Temperature Prediction Model Based on Bayesian Regularization BP Neural Network

2015 ◽  
Vol 740 ◽  
pp. 871-874
Author(s):  
Hui Zhao ◽  
Li Rong Shi ◽  
Hong Jun Wang
Keyword(s):  
Neural Network ◽  
Network Structure ◽  
Bp Neural Network ◽  
Principal Component ◽  
Coupling Factor ◽  
Bayesian Regularization ◽  
The Neural Network ◽  
Neural Network Structure ◽  
Input Variables ◽  
Bp Neural Network Model

Directing against the problems of too large size of the neural network structure due to the existence of a complex relationship between the input coupling factor and too many input factors in establishing model for predicting temperature of sunlight greenhouse. This article chose the environmental factors that affect the sunlight greenhouse temperature as data sample. Through the principal component analysis of data samples, three main factors were extracted. These selected principal component values were taken as the input variables of BP neural network model. Use the Bayesian regularization algorithm to improve the BP neural network. The empirical results show that this method is utilized modify BP neural network, which can simplify network structure and smooth fitting curve, has good generalization capability.

scholarly journals Optimization of sand casting performance parameters and missing data prediction

2019 ◽  
Vol 6 (8) ◽  
pp. 181860 ◽  
Author(s):  
Qingwei Xu ◽  
Kaili Xu ◽  
Li Li ◽  
Xiwen Yao
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Bp Neural Network ◽  
Grey Relational Analysis ◽  
Sand Casting ◽  
Performance Parameters ◽  
Relational Analysis ◽  
Data Prediction ◽  
Missing Data Prediction ◽  
Grey Relational

Due to a wide range of applications, sand casting occupies an important position in modern casting practice. The main purpose of this study was to optimize the performance parameters of sand casting based on grey relational analysis and predict the missing data using back propagation (BP) neural network. First, the influence of human factors was eliminated by adopting the objective entropy weight method, which also saved manpower. The larger variation degree in the evaluation indicators, indicating that the evaluated projects had good discrimination in this regard, the larger weight should be given to these evaluation indicators. Second, the performance parameters of sand casting were optimized based on grey relational analysis, providing a reference for sand milling. The larger the grey relational degree, the closer the evaluated project was to the ideal project. Third, this paper provided a new method for determining the number of hidden neurons in a network according to the mean square error of training samples, and venting quality was predicted based on BP neural network. The relevant theory was deduced before predicting missing data, such that there will be a general understanding regarding the prediction principle of BP neural network. Fourth, to demonstrate the validity of BP neural network adopted in the process of missing data prediction, grey system theory was applied to compare the result of missing data prediction.

Based on the Genetic Algorithm to Optimize the BP Neural Network in the Degree of Concrete Creep Prediction Model

2014 ◽  
Vol 584-586 ◽  
pp. 1346-1350
Author(s):  
Hui Qin Yao ◽  
Ye Long Jiang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Reference Value ◽  
Concrete Creep ◽  
Practical Engineering ◽  
Input Variables ◽  
Bp Neural Network Model

The BP neural network model for creep degree of concrete is established,in which concrete age under load and loading time as input variables and the creep degree of concrete as output variable. In order to get best optimal weight and threshold of the BP neural network, genetic algorithms method has been used by selection, crossover and mutation operation. The BP neural network model is applied to the engineering of “515”dam.Comparison the prediction results of the BP neural network and the eight-parameters formula of concrete creep degree, the BP neural network model has high prediction accuracy. What’s more, this intelligent prediction model for creep degree of concrete has good credibility and reference value in practical engineering.

scholarly journals Study on Prediction of Coal-Gas Compound Dynamic Disaster Based on GRA-PCA-BP Model

Geofluids ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kai Wang ◽  
Kangnan Li ◽  
Feng Du
Keyword(s):  
Neural Network ◽  
Principal Component Analysis ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Grey Relational Analysis ◽  
Principal Component ◽  
Coal Gas ◽  
Relational Analysis ◽  
Dynamic Disaster ◽  
Grey Relational

The intensity and depth of China’s coal mining are increasing, and the risk of coal-gas compound dynamic disaster is prominent, which seriously restricts the green, safe, and efficient mining of China’s coal resources. How to accurately predict the risk of disasters is an important basis for disaster prevention and control. In this paper, the Pingdingshan No. 8 coal mine is taken as the research object, and the grey relational analysis (GRA), principal component analysis (PCA), and BP neural network are combined to predict the coal-gas compound dynamic disaster. First, the weights of 13 influencing factors are sorted and screened by grey relational analysis. Next, principal component analysis is carried out on the influencing factors with high weight value to extract common factors. Then, the common factor is used as the input parameter of BP neural network to train the previous data. Finally, the coal-gas compound dynamic disaster prediction model based on GRA-PCA-BP neural network is established. After verification, the model can effectively predict the occurrence of coal-gas compound dynamic disaster. The prediction results are consistent with the actual situation of the coal mine with high accuracy and practicality. This work is of great significance to ensure the safe and efficient production of deep mines.

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