scholarly journals Risk Prediction of Sports Events Based on Gray Neural Network Model

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhihui Wang
Keyword(s):  
Neural Network ◽  
Fuzzy Theory ◽  
Network Modeling ◽  
Sports Events ◽  
Event Risk ◽  
Risk Warning ◽  
Operation Process ◽  
Sample Data ◽  
Warning Model ◽  
Predictive Research

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.

scholarly journals Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zijun Dang ◽  
Shunshun Liu ◽  
Tong Li ◽  
Liang Gao
Keyword(s):  
Neural Network ◽  
Optical Flow ◽  
Human Behavior ◽  
Temporal Relationship ◽  
Recognition Method ◽  
Risk Warning ◽  
Neural Network Algorithm ◽  
Feature Information ◽  
Relationship Network ◽  
Warning Model

In this paper, a deep confidence neural network algorithm is used to design and deeply analyze the risk warning model for stadium operation. Many factors, such as video shooting angle, background brightness, diversity of features, and the relationship between human behaviors, make feature attribute-based behavior detection a focus of researchers’ attention. To address these factors, researchers have proposed a method to extract human behavior skeleton and optical flow feature information from videos. The key of the deep confidence neural network-based recognition method is the extraction of the human skeleton, which extracts the skeleton sequence of human behavior from a surveillance video, where each frame of the skeleton contains 18 joints of the human skeleton and the confidence value estimated for each frame of the skeleton, and builds a deep confidence neural network model to classify the dangerous behavior based on the obtained skeleton feature information combined with the time vector in the skeleton sequence and determine the danger level of the behavior by setting the corresponding threshold value. The deep confidence neural network uses different feature information compared with the spatiotemporal graph convolutional network. The deep confidence neural network establishes the deep confidence neural network model based on the human optical flow information, combined with the temporal relational inference of video frames. The key of the temporal relationship network-based recognition method is to extract some frames from the video in an orderly or random way into the temporal relationship network. In this paper, we use several methods for comparison experiments, and the results show that the recognition method based on skeleton and optical flow features is significantly better than the algorithm of manual feature extraction.

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.

Risk Warning of High-Tech Enterprise Independent Innovation Based on Chaotic Neural Network

2013 ◽  
Vol 568 ◽  
pp. 179-185
Author(s):  
Zhi Yong Wu ◽  
Hong Mei Chen ◽  
Xiu Hui Qi
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Evaluation Index System ◽  
High Tech ◽  
Independent Innovation ◽  
Innovation Activities ◽  
Network Training ◽  
Risk Warning ◽  
Warning Model

Risk warning evaluation index system of independent innovation is established according to the process of innovation activities of high-tech enterprise, Chaotic Analysis Method is introduced into the BP(Back Propagation)Neural Network Model to research the early risk warning of high-tech enterprises independent innovation, the empirical results show that the integration of early warning model is feasible and effective, and significantly improve the convergence speed of network training, to some extent, avoid getting into local minimum.

Research of Ship-Bridge Collision early Warning on the Basis of Fuzzy Neural Networks

2012 ◽  
Vol 594-597 ◽  
pp. 2847-2852
Author(s):  
Xue Hui Yang ◽  
Xin Zhou
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Fuzzy Theory ◽  
Fuzzy Neural Networks ◽  
Basic Principles ◽  
Early Warning Model ◽  
Network Method ◽  
Fuzzy Neural ◽  
Warning Model ◽  
Practical Accuracy

The paper establishes the ship-bridge collision early warning model on the basis of Fuzzy Theory and neural network method, puts forward the basic principles and concrete process of the ship-bridge collision early warning method. The model and calculation method applied in VTS after test and verification, it can achieve the practical accuracy and effect and finally achieve the active ship-bridge collision early warning.

Fuzzy Neural Network Modeling for Fault Diagnosis in Turbine Startup of a Power Plant

2005 ◽  
Author(s):  
Weiliang Chen ◽  
Guodong Xia ◽  
Hongyu Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Plant ◽  
Neural Network Model ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Fuzzy Neural ◽  
Mode A

A fault set and a symptom set were established in order to exactly judge and to quickly dispose in turbine startup of a power plant. There are ten typical faults in the fault set and sixteen fault symptoms in the symptom set. In consideration of the various kinds of change directions and ranges of the fault symptom parameters, the fuzzy disposal of nine degrees is put forward to build a set of typical fault-character-sample mode. A neural network model for fault diagnosis was obtained by fuzzy theory and radial basis function, and it was validated by using evaluator. It shows that the fuzzy fault disposal and the swiftness of training constringency are very satisfied in turbine startup of this power plant.

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