The Risk Early-Warning Model of Financial Operation in Family Farms Based on Back Propagation Neural Network Methods

2021 ◽  
Author(s):  
Zhigui Guan ◽  
Yuanjun Zhao ◽  
Guojing Geng
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Family Farms ◽  
Early Warning Model ◽  
Network Methods ◽  
Warning Model ◽  
Financial Operation

scholarly journals Safety Warning Model of Coal Face Based on FCM Fuzzy Clustering and GA-BP Neural Network

Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1082
Author(s):  
Fanqiang Meng
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Early Warning ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Coal Face ◽  
Early Warning Model ◽  
Working Face ◽  
The Neural Network ◽  
Warning Model

Risk and security are two symmetric descriptions of the uncertainty of the same system. If the risk early warning is carried out in time, the security capability of the system can be improved. A safety early warning model based on fuzzy c-means clustering (FCM) and back-propagation neural network was established, and a genetic algorithm was introduced to optimize the connection weight and other properties of the neural network, so as to construct the safety early warning system of coal mining face. The system was applied in a coal face in Shandong, China, with 46 groups of data as samples. Firstly, the original data were clustered by FCM, the input space was fuzzy divided, and the samples were clustered into three categories. Then, the clustered data was used as the input of the neural network for training and prediction. The back-propagation neural network and genetic algorithm optimization neural network were trained and verified many times. The results show that the early warning model can realize the prediction and early warning of the safety condition of the working face, and the performance of the neural network model optimized by genetic algorithm is better than the traditional back-propagation artificial neural network model, with higher prediction accuracy and convergence speed. The established early warning model and method can provide reference and basis for the prediction, early warning and risk management of coal mine production safety, so as to discover the hidden danger of working face accident as soon as possible, eliminate the hidden danger in time and reduce the accident probability to the maximum extent.

scholarly journals Analysis and Simulation of the Early Warning Model for Human Resource Management Risk Based on the BP Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xue Yan ◽  
Xiangwu Deng ◽  
Shouheng Sun
Keyword(s):  
Neural Network ◽  
Resource Management ◽  
Human Resource Management ◽  
Human Resource ◽  
Early Warning ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Bp Network ◽  
Early Warning Model ◽  
Warning Model

Human resource management risks are due to the failure of employer organization to use relevant human resources reasonably and can result in tangible or intangible waste of human resources and even risks; therefore, constructing a practical early warning model of human resource management risk is extremely important for early risk prediction. The back propagation (BP) neural network is an information analysis and processing system formed by using the error back propagation algorithm to simulate the neural function and structure of the human brain, which can handle complex and changeable things that do not have an obvious linear relationship between output results and input factors, so as to find the objective connection between the two. Based on the summary and analysis of previous research works, this article expounded the research status and significance of early warning for human resource management risks, elaborated the development background, current status, and future challenges of the BP neural network, introduced the method and principle of the BP neural network’s connection weight calculation and learning training, performed the risk inducement analysis, index system establishment, and network node selection of human resource management, constructed an early warning model of human resource management risk based on the BP neural network, conducted the risk warning model training and detection based on the BP neural network, and finally carried out a simulation and its result analysis. The study results show that the early warning model of human resource management risk based on the BP network is effective, and this trained and tested BP network risk warning model can be used to conduct early warning empirical research on human resource risks to prevent human resource risks, ensure enterprise’s benign operation, and at the same time play a role in supervision and promotion of market order rectification.

Research on construction of anti-dumping early warning model based on BP neural network

2020 ◽  
Vol 39 (4) ◽  
pp. 5649-5659
Author(s):  
Jun Chen ◽  
Ying Xu ◽  
Shiyan Xu ◽  
Chenyang Zhao ◽  
Hui Chen
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Bp Neural Network ◽  
The United States ◽  
Indicator System ◽  
Actual Case ◽  
The Sustainable Development ◽  
Early Warning Model ◽  
Input Layer ◽  
Warning Model

China has now become the country with the most anti-dumping lawsuits in the world, and the trade protection of anti-dumping measures has become a huge obstacle to the sustainable development of China’s foreign trade. In view of the current situation of anti-dumping in the United States, this study combines BP neural network to construct an anti-dumping early warning model. In order to predict the longer-term future based on the existing database, the BP neural network should be used to predict the indicators in the existing index database, and then the predicted warning indicator system is used as the input layer to warn the future police. Moreover, this study conducts research on the performance of the algorithm based on the actual case analysis. The research shows that the algorithm has certain effects and can provide theoretical reference for subsequent related research.

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