scholarly journals THE EARLY WARNING MODEL OF TRACK AND FIELD SPORTS BASED ON RBF NEURAL NETWORK ALGORITHM

2021 ◽  
Vol 27 (5) ◽  
pp. 523-526
Author(s):  
Heqiong Wen
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Injury Risk ◽  
Rbf Neural Network ◽  
Track And Field ◽  
Survey Method ◽  
Early Warning Model ◽  
Model Based ◽  
Warning Model ◽  
Field Sports

ABSTRACT Background: Athletics plays a very important role in competitive sports. The strength of track and field directly represents the level of a country's sports competition. Objective: This work aimed to study the track and field sports forewarning model based on radial basis function (RBF) neural networks. One hundred outstanding athletes were taken as the research objects. The questionnaire survey method was adopted to count athletes’ injury risk factors, and coaches were consulted to evaluate the questionnaire's overall quality, structure, and content. Methods: A track and field early warning model based on RBF neural network is established, and the results are analyzed. Results: The results showed that the number of people who thought the questionnaire was relatively complete (92%) was considerably higher than that of very complete (2%) and relatively complete (6%) (P<0.05). The number of people who thought that the questionnaire structure was relatively perfect (45%) was notably higher than that of the very perfect (18%) (P<0.05). The semi-reliability test result suggested that the questionnaire reliability was 0.85. Tests on ten samples showed that the RBF neural network model error and the actual results were basically controlled between −0.04~0.04. Conclusions: After the sample library test, the track and field sports forewarning model under RBF neural network can obtain relatively favorable results. Level of evidence II; Therapeutic studies - investigation of treatment results.

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.

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