Risk Prediction of Sports Events Based on Gray Neural Network Model
In this paper, neural network is used as a predictive network modeling method, with the support of MATLAB Neural Toolbox, based on the implementation of predictive research. A risk warning model is designed for sports events relying on neural network s to reduce the losses caused by risk accidents....
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6214036 |
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| _version_ | 1832549647347875840 |
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| author | Zhihui Wang |
| author_facet | Zhihui Wang |
| author_sort | Zhihui Wang |
| collection | DOAJ |
| description | In this paper, neural network is used as a predictive network modeling method, with the support of MATLAB Neural Toolbox, based on the implementation of predictive research. A risk warning model is designed for sports events relying on neural network s to reduce the losses caused by risk accidents. First, the article introduces a literature review of sports event risk warning, combined with the sports event risk warning index system; summarizes the main advantages of using neural network and fuzzy theory; and establishes a sports event risk warning model relied on neural network. The article starts with the application of gray network in sports risk warning design, starting from the necessity of applying gray network in sports event risk warning; analyzes the risk warning model and operation process; and conducts sample data verification to verify this power of the model. Practice has proved that the application of gray neural network in sports events can play a role in risk warning. |
| format | Article |
| id | doaj-art-48c9c8e61ce84176a2412e774e90ef59 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-48c9c8e61ce84176a2412e774e90ef592025-02-03T06:10:45ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/62140366214036Risk Prediction of Sports Events Based on Gray Neural Network ModelZhihui Wang0Department of Sports, Inner Mongolia University for the Nationalities, Tongliao 028000, ChinaIn this paper, neural network is used as a predictive network modeling method, with the support of MATLAB Neural Toolbox, based on the implementation of predictive research. A risk warning model is designed for sports events relying on neural network s to reduce the losses caused by risk accidents. First, the article introduces a literature review of sports event risk warning, combined with the sports event risk warning index system; summarizes the main advantages of using neural network and fuzzy theory; and establishes a sports event risk warning model relied on neural network. The article starts with the application of gray network in sports risk warning design, starting from the necessity of applying gray network in sports event risk warning; analyzes the risk warning model and operation process; and conducts sample data verification to verify this power of the model. Practice has proved that the application of gray neural network in sports events can play a role in risk warning.http://dx.doi.org/10.1155/2021/6214036 |
| spellingShingle | Zhihui Wang Risk Prediction of Sports Events Based on Gray Neural Network Model Complexity |
| title | Risk Prediction of Sports Events Based on Gray Neural Network Model |
| title_full | Risk Prediction of Sports Events Based on Gray Neural Network Model |
| title_fullStr | Risk Prediction of Sports Events Based on Gray Neural Network Model |
| title_full_unstemmed | Risk Prediction of Sports Events Based on Gray Neural Network Model |
| title_short | Risk Prediction of Sports Events Based on Gray Neural Network Model |
| title_sort | risk prediction of sports events based on gray neural network model |
| url | http://dx.doi.org/10.1155/2021/6214036 |
| work_keys_str_mv | AT zhihuiwang riskpredictionofsportseventsbasedongrayneuralnetworkmodel |