Grey BP Neural Network Research Articles

Application of grey BP neural network in port logistics demand analysis

From the perspective of port cargo throughput, this paper firstly analyses the characteristics and influencing factors of port logistics demand. Secondly, considering the characteristics of nonlinear logistics demand and small sample modelling, the modelling adopts GM(1, 1) and the single prediction model of BP neural network for calculation. Then, based on the prediction results and the target of minimum fitting prediction square-error, the single model is given weight, and the combined prediction model is constructed. Finally, taken Qingdao Port as an example, the port logistics demand is simulated by MATLAB software. The results show that the combined forecasting model has higher accuracy and stronger stability than the single forecasting model, which can effectively reduce the error rate and make the forecasting result closer to reality, thus having guiding significance for the future port logistics development planning.

Gray BP neural network based prediction of rice protein interaction network

In order to improve the effectiveness of the network prediction result of rice protein interaction, the network prediction method of rice protein interaction based on gray BP neural network has been proposed. Firstly, a series of key features about interaction sites of rice protein such as the series spectrum, conserved weight, entropy value, accessible exterior product of compound and sequence rate, etc., should be extracted firstly. Then the gray BP neural network and their integration will be applied to the training and test of these sample set. Ten times of cross validation are applied to the training and test, and four groups of feature combinations of rice protein interaction with comparability are created. As for each time of the addition of new features in the experiment, the accurate prediction rate would be improved, especially in case of the addition of exterior product and sequence rate features, the accuracy can be improved greatly, which means that in case the combination of multiple features is adopted, the method of predicting the interaction of rice protein by combining the gray BP neural network algorithm is accurate and effective.

Forecast of Total Energy Consumption in Shandong Province Based on Grey BP Model

With the continuous increase of China's total energy consumption, we can find the rule and grasp its development trend from the change trend of energy consumption. In order to provide scientific basis for rational use of energy. In this paper,firstly, based on the data of total energy consumption of shandong province from 2007 to 2016, grey prediction model and BP neural network were used to predict total energy consumption of shandong province from 2007 to 2016. MATLAB was used to calculate the predicted value of each year and the average relative error of the two models was 7.25% and 3.70% respectively. Secondly, on the basis of the grey prediction model, BP neural network was used to correct the predicted value of total energy in shandong province. Then, the grey BP modified model was used to obtain the total energy consumption of shandong province from 2007 to 2016. MATLAB was used to calculate the predicted values of each year and the average relative error of the modified model was 2.04%. Finally, the total energy consumption of shandong province in 2018-2035 is predicted. The results show that the average relative error is small and the prediction effect is obvious. This shows that the grey BP model is effective in predicting total energy consumption.

Prediction of construction cost index based on multi variable grey neural network model

The construction cost index can reflect the construction market price changes on the project cost. Due to unsuitable way of management and the consideration of data security, there is the possibility of researchable sample data missing, or the possibility of not enough data. This way many classical prediction models cannot function well in practice. The study compared numerous models. According to the characteristics of poor sample data and numerous factors for construction engineering, the grey model and BP neural network were selected to establish the prediction model. This study built material and machinery shift as independent variables, the construction cost as the dependent variable to establish the grey neural network model. In empirical analysis, data processing, training and simulation were used by MATLAB, the results concluded: compared with other conventional models, the combined forecasting model for poor data is more effective.

Application of Gray B6 P Neural Network in Henan Coal Demand Forecast

This paper studied the coal demand in the prediction accuracy problems. The traditional gray GM(1,1)model has the theoretical prediction problem of poor accuracy which leaded to less accurate prediction. A modified gray BP Neural Network forecasting model was used to predict the residual correction. The total consumption of coal as a major factor in variables was selected to construct forecast of coal demand The simulation results show that the proposed algorithm has better prediction accuracy and is an effective demand forecasting algorithm

Study of a Gray Genetic BP Neural Network Model in Fault Monitoring and a Diagnosis System for Dam Safety

In this paper, a self-diagnosis system of observer fault with linear and non-linear combination is studied in light of the unstable performance of the automatic monitoring system and the drift of the measured value. The system makes a prediction step ahead of time, compares it with the online measured value, and makes a logical judgment based on the residual error to achieve the purpose of real-time diagnosis of the automatic monitoring system. We developed a novel combined algorithm for dam deformation prediction using two traditional models and one optimization model. The developed algorithm combines two sub-algorithms: the gray model (GM) (1, 1) and the back-propagation neural network (BPNN) model. The GM (1, 1) addresses the effects of the automated monitoring of data from unstable situations; the BPNN model addresses the internal non-linear regularity of the dam displacement. The connection weights and thresholds of the BPNN model can be optimized and determined via the genetic algorithm (GA), which can decrease the uncertainties within the model predictions and improve the prediction accuracy. The results show that the fault self-diagnosis system based on the GM-GA-BP combined model can realize online fault diagnosis better than the traditional single models.

Application of grey systems in predicting the degree of cotton spider mite infestations

PurposeMany factors affect the emergence and development of crop diseases and insect pests. Traditional methods for investigating this subject are often difficult to employ and produce limited data with considerable uncertainty. The purpose of this paper is to predict the annual degree of cotton spider mite infestations by employing grey theory.Design/methodology/approachThe authors established a GM(1,1) model to forecast mite infestation degree based on the analysis of historical data. To improve the prediction accuracy, the authors modified the grey model using Markov chain and BP neural network analyses. The prediction accuracy of the GM(1,1), Grey-Markov chain, and Grey-BP neural network models was 84.31, 94.76, and 96.84 per cent, respectively.FindingsCompared with the single grey forecast model, both the Grey-Markov chain model and the Grey-BP neural network model had higher forecast accuracy, and the accuracy of the latter was highest. The improved grey model can be used to predict the degree of cotton spider mite infestations with high accuracy and overcomes the shortcomings of traditional forecasting methods.Practical implicationsThe two new models were used to estimate mite infestation degree in 2015 and 2016. The Grey-Markov chain model yielded respective values of 1.27 and 1.15, whereas the Grey-BP neural network model yielded values 1.4 and 1.68; the actual values were 1.5 and 1.8.Originality/valueThe improved grey model can be used for medium- and long-term predictions of the occurrence of cotton spider mites and overcomes problems caused by data singularity and fluctuation. This research method can provide a reference for the prediction of similar diseases.

Forecast Model of Coal Demand Based on Improved Tandem Gray BP Neural Network

Forecast Model of Coal Demand Based on Improved Tandem Gray BP Neural Network

Performance Prediction Model of University Students Based on the Grey BP Neural Network

Performance Prediction Model of University Students Based on the Grey BP Neural Network

The Forecasting of Humanitarian Supplies Demand Based on Gray Relational Analysis and BP Neural Network

This paper analyzes the characteristics of humanitarian supplies demand in the context of flood and discusses the disasters associated factors which influence the demand of humanitarian supplies. Then we choose the severe flooding whose grades is more than fifty year return period between 2004 and 2016 as the analysis objects，which is illustrated by the example of the Red Cross Society of China whose demand of relief tent in the flood. Finally, we set up gray relational analysis and BP neural network.

Application of GEP Algorithm in Prediction of Subgrade Settlement

The development law of subgrade settlement is highly nonlinear, unstable and uncertain, so the prediction of subgrade settlement is a difficult problem. In this paper, a prediction model of subgrade settlement based on gene expression programming algorithm (GEP) is proposed, and the algorithm compared with the gray level GM (1,1) model and BP neural network model. The results show that the prediction accuracy of subgrade settlement prediction model that is put forward is better than that of GM (1,1) model and BP neural network model, which provides a new method for subgrade settlement prediction and is of practical significance.

Prediction Model of Sports Performance Based on Grey BP Neural Network

Prediction Model of Sports Performance Based on Grey BP Neural Network

Grey System and BP Neural Network Model for Industrial Economic Forecasting

Grey System and BP Neural Network Model for Industrial Economic Forecasting

Research and Application of Improved Gas Concentration Prediction Model Based on Grey Theory and BP Neural Network in Digital Mine

Gas disaster is one of the most serious disasters in coal mine safety production. Therefore, it is of great significance to strengthen the coal mine gas disasters forecasting warning technology research for improving the ability of prevention and control gas disaster in coal mine and promoting the development of digital mine in our country. The biggest characteristics of using grey prediction model GM (1,1) is that the algorithm is quite simple, and also, when building the model, less data can be used. It is convenient for modelling and operation, but the effect of forecast of the grey prediction model for systems with volatility is not very ideal, and the prediction accuracy will reduce gradually along with the extrapolation of time. BP neural network has a good performance for prediction of nonlinear system, but when the network was trained, it often requires large amounts of data. This paper is based on the grey prediction model, using advantages of grey prediction that the model algorithm is simple and the procedure of model building needs less data, and the BP neural network that the performance of grey forecast model for nonlinear system prediction is good. We revise the grey prediction model through BP neural network and finally we build an improved gas concentration prediction model based on grey theory and BP neural network, and carry out a specific computer simulation. Results show that the model effectively improved the precision of gas prediction.

Short-Term Forecast of Blast Furnace Gas Production Amount Based on Grey RBF Neural Network

Dynamic optimization scheduling of the gas in iron and steel enterprises has great significance to reduce gas emission and the short-term forecast is the premise to realize the energy dynamic scheduling. Based on the characteristics that the influencing factors of blast furnace gas amount are complex and difficult to collect, a grey radial basis function (RBF) neural network forecast model is proposed to predict the gas amount for blast furnace in this paper. Combining grey theory, which is used to preprocess the historical data and obtain abundant information, with RBF neural network makes the effective trend forecast in the next 30 minutes come true. The model proposed in this paper is proved to be more accurate according to control experiments against the grey BP neural network.

Application of Grey Prediction and BP Neural Network in Hydrologic Prediction

Based on the data of annual runoff at Boluo Hydrologic Station in the Dongjiang River of Guangdong Province from 1954 to 2010, this paper establishes a prediction model of an annual runoff through three methods, that is, the regression analysis, the grey theory and the neural network. The prediction model which is established by the regression analysis method has passed F-statistical test (P=0.05), but the relative error of predicted value at 30% of the data point is over ±30%, and its prediction precision is general. The precision of the residual pre- diction model of an annual runoff GM (1, 1) that established based on the grey theory is obviously better than that of the former one; the relative error of predicted value at only 10% of the data point is over ±30%; the Nash statistical coefficient (NS) of predicted value and measured value is 0.627, and the correlation coefficient (R 2 ) is 0.774. For the prediction model of BP neural network, the relative error at about 5% of the data point is over ±30%, and NS=0.66, R 2 =0.853. In general, the precision and the reliability of the neural network prediction model of an annual runoff are the best.

Improved BP Neural Network Model Applied in Settlement Analysis

According to the settlement and deformation of pavement in subway construction, the paper improved on the basis of gray system theory and BP neural network forecasting model. The method is adapted to modified residual prediction model to predict with engineering examples. It provides decision-making basis for construction engineering and monitoring.

Research of EDM Titanium Alloy TC11 Based on GM(1,N) Model and BP Neural Network

In order to predict the influences of electrical discharge machining (EDM) and enhance material removal rate (MRR). A model was presented for predictions of MRR in EDM TC11 test piece based on the construction method of gray GM(1,N) model and BP neural network model (GNNM).The results show that the GNNM is credible with the maximum absolute error 3.58%, the minimum absolute error 0.01% and the mean error 0.53%were obtained .The results indicated that the model can reflect the technological law of EDM TC11 and successfully predict its processing speed .The paper provides references and basis for selecting processing parameters of EDM TC11 and has practical significance.

Railway Freight Turnover Forecast Based on the BP Neural 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.

Predication Method for China's Civil Aviation Fuel Consumption

With the China’s civil aviation industry gradual market-oriented and the rapid development of China’s economy, China’s civil aviation transportation fuel consumption has grown significantly in nearly past three decades. Therefore, it’s a very important strategic significance of the prediction of China’s civil aviation transportation fuel consumption. In this paper, gray system and neural network approach, combined with China’s civil aviation industry 1980-2010 total traffic volume of the data, we establish gray system GM (1,1) model and BP neural network model for civil aviation transport volume. Training and simulation of the back propagation neutral network model and the gray system GM(1,1) used MATLAB. BP neural network modeling takes into account in three factors: the number of aircraft aviation industry, flight hours and total turnover. The fitting precision of the gray system GM(1,1) model is 64.2% while the fitting precision of the back propagation neutral network model is 90.16%. Thus, the back propagation neutral network model is better for estimating Civil aviation fuel consumption.