Fracturings and Plug Early-Warning Model Based on Improved BP Neural Network Using Conjugate Gradient

2016 ◽  
Vol 13 (11) ◽  
pp. 8522-8528 ◽  
Author(s):  
Liang Haibo ◽  
Wan Yongsheng ◽  
Li Mengxi ◽  
Cheng Xiangzhen
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Conjugate Gradient ◽  
Bp Neural Network ◽  
Early Warning Model ◽  
Model Based ◽  
Warning Model

Early Warning Model of Enterprise Operating Ability Using BP Neural Network

2010 ◽  
Vol 20-23 ◽  
pp. 948-953 ◽  
Author(s):  
Mo Yu Wang ◽  
Jie Chen ◽  
Xiao Liu Shen ◽  
Gui Lin Yu
Keyword(s):  
Neural Network ◽  
Electric Power ◽  
Early Warning ◽  
Bp Neural Network ◽  
Important Work ◽  
Accurate Analysis ◽  
Early Warning Model ◽  
Model Based ◽  
Increasing Risk ◽  
Warning Model

With the increasing risk in electric power bureaus, warning risk of enterprise operating ability in advance is an important work. However it is very difficult to establish stable functions to describe the mapping relationship between operating ability and associated causal influences. Hence, early warning of the operating ability is harder. In this paper, an early warning model based on BP neural network is designed and put forward to forecast the risk of operating ability of an electric power bureau. In addition, illustration by the experiment is given. The stable and accurate analysis result of the experiment shows that this early warning model is applicable to forecast the risk of operating ability of electric power bureaus.

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.

Application of Artificial Neural Network in Corporate Financial Risk Early-Warning

2013 ◽  
Vol 336-338 ◽  
pp. 2476-2479 ◽  
Author(s):  
Hong Xiao Zhou ◽  
Sai Hua Xu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Bp Neural Network ◽  
Financial Risk ◽  
Financial Data ◽  
Early Warning Model ◽  
Risk Warning ◽  
Artificial Neural ◽  
Warning Model

The traditional financial risk warning model are all based on probability theory and statistical analysis, but the precisions of the results are usually not satisfied in practice. This paper studies the application of artificial neural network in corporate financial risk early-warning. It designs an early warning model of financial risk based on BP neural network. And then selects financial data from 30 enterprises as samples to train and test the network. The result indicates that the risk early warning model is very effective. It can solve some problems of the traditional early warning methods such as difficult to deal with highly non-linear and lack of adaptive capacity.

scholarly journals Research on Early Warning Model of College Students' Psychological Crisis Based on Genetic BP Neural Network

2019 ◽  
Vol 8 (6) ◽  
pp. 112
Author(s):  
Jia Wang ◽  
Zijie Zhang ◽  
Haiji Luo ◽  
Yinghao Liu ◽  
Wei Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
College Students ◽  
Early Warning ◽  
Bp Neural Network ◽  
Early Warning Model ◽  
Warning Model
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