A Grain Output Combination Forecast Model Modified by Data Fusion Algorithm

2018 ◽  
Vol 27 (2) ◽  
pp. 303-315
Author(s):  
Xiang Wan ◽  
Bing-Xiang Liu ◽  
Xing Xu
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Bp Neural Network ◽  
Particle Swarm Optimization Algorithm ◽  
Back Propagation ◽  
Forecast Model ◽  
Fusion Algorithm ◽  
Combination Model ◽  
Combination Forecast ◽  
Use Of Data

AbstractTo deal with the lack of accuracy and generalization ability in some single models, grain output models were built with lots of relevant data, based on the powerful non-linear reflection of the back-propagation (BP) neural network. Three kinds of grain output models were built and took advantage of – particle swarm optimization algorithm, mind evolutionary algorithm, and genetic algorithm – to optimize the BP neural network. By the use of data fusion algorithm, the outcomes of different models can be modified and fused together, and the combination-predicted outcome can be obtained finally. Taking advantage of this combination model to predict the total grain output of China, the results showed that the total grain output in 2015 was a bit larger than the actual value of about 0.0115%. It was much more accurate than the three single models. The experimental results verify the feasibility and validity of the combination model.

FA-BP Neural Network-Based Forecast for Railway Passenger Volume

2014 ◽  
Vol 641-642 ◽  
pp. 673-677
Author(s):  
Meng Tian Li ◽  
Xiang Feng Ji ◽  
Jian Zhang ◽  
Bin Ran
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Forecast Model ◽  
Original Data ◽  
Volume Data ◽  
Model Based ◽  
Forecast Models ◽  
Railway Passenger ◽  
Bp Neural Network Model

The research presents a long-term forecast model based on the use of a back-propagation (BP) neural network. Firstly, a brief overview of the forecast models and BP neural network model is demonstrated. Then the improved BP model based on factor analysis (FA-BP) and algorithmfor solving the model are presented. At last, a numerical case study is shown.As the current statistic yearbook only provides the volume data of Jing-Hu corridor, the notion of economical relation intensityis applied to process the original data. The results show that FA-BP neural network is effective in forecast. The proposed model providesa reference in the forefront field of integrated regional transportation planning.

Applied-Information Technology in Short-Term Wind Speed Forecast Model for Wind Farms Based on Ant Colony Optimization and BP Neural Network

2014 ◽  
Vol 662 ◽  
pp. 259-262 ◽  
Author(s):  
Qi Di Zhao ◽  
Yang Yu ◽  
Meng Meng Jia
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Ant Colony Optimization ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Wind Farms ◽  
Forecast Model ◽  
Ant Colony ◽  
Short Term

To improve the short-term wind speed forecasting accuracy of wind farms, a prediction model based on back propagation (BP) neural network combining ant colony algorithm is built to predict short-term wind speed. The input variables of BP neural network predictive model are historical wind speeds, temperature, and air pressure. Ant colony algorithm is used to optimize the weights and bias of BP neural networks. Using the ant colony optimization BP neural network model to predict the future 1h wind speed, the simulation results show that the proposed method offers the advantages of high precision and fast convergence in contrast with BP neural network.

Research on the Combination Model of Short-Term Traffic Flow Forecasting

2012 ◽  
Vol 178-181 ◽  
pp. 2668-2672
Author(s):  
Yuan Lin Liu ◽  
Wu Sheng Hu ◽  
Su Lan Li ◽  
Hong Wei Li
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Traffic Flow ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Model Error ◽  
Short Term ◽  
Combination Model ◽  
Input Layer

Short-term traffic flow is difficult to predict accurately and real-time, owing to the characteristics of very complexity, randomness, nonlinearity and uncertainty, etc.. In this paper, the method of combining multiple linear regression with back propagation (BP) neural network was proposed, using BP neural network to compensate the model error of multiple linear regression. The combination model and the corresponding algorithm program was made, and used to pedict the short-term traffic flow. Two different methods of selecting the input layer parameters were used and compared, while the new method has higher accuracy and stability.

scholarly journals Prediction Of Water Quality Of Bicarbonate Mineral Water In WUDALIANCHI Based on BP Neural Network Model

2018 ◽  
Vol 53 ◽  
pp. 04017
Author(s):  
ZENG Ying ◽  
YANG Chen ◽  
WANG Xiying ◽  
FANG Zhenxing ◽  
WU Jing
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Bp Neural Network ◽  
Mineral Water ◽  
Forecast Model ◽  
Soluble Solids ◽  
Water Type ◽  
Combination Forecast ◽  
Change Trend

In order to grasp the water quality change trend and predict the future water quality characteristics of the bicarbonate mineral water in WUDALIANCHI, using the measured data from 2008 to 2016 of north drink spring in WUDALIANCHI as the predicted sample, carbon dioxide, total soluble solids, strontium and metasilicic acid which can divide mineral water type as analysis factor, the BP neural network combination forecast model was contructed. The results showed that the BP neural network combination forecast model was obviously more precise and better than grey system model, its average relative error was controlled within 5%. The results indicated that the BP neural network combination forecast model can effectively predict the change trend of water quality of bicarbonate mineral water in WUDALIANCHI.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

scholarly journals Assessment of water resource security in karst area of Guizhou Province, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liying Liu
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Water Resource ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Water Shortage ◽  
Guizhou Province ◽  
Karst Area ◽  
Water Resource Security ◽  
Resource Security

AbstractThis paper presents the assessment of water resource security in the Guizhou karst area, China. A mean impact value and back-propagation (MIV-BP) neural network was used to understand the influencing factors. Thirty-one indices involving five aspects, the water quality subsystem, water quantity subsystem, engineering water shortage subsystem, water resource vulnerability subsystem, and water resource carrying capacity subsystem, were selected to establish an evaluation index of water resource security. In addition, a genetic algorithm and back-propagation (GA-BP) neural network was constructed to assess the water resource security of Guizhou Province from 2001 to 2015. The results show that water resource security in Guizhou was at a moderate warning level from 2001 to 2006 and a critical safety level from 2007 to 2015, except in 2011 when a moderate warning level was reached. For protection and management of water resources in a karst area, the modes of development and utilization of water resources must be thoroughly understood, along with the impact of engineering water shortage. These results are a meaningful contribution to regional ecological restoration and socio-economic development and can promote better practices for future planning.

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