scholarly journals Model Study for Early Warning System of Urban Road Intersection Based on the Back Propagation Neural Network

2016 ◽  
Vol 4 (1) ◽  
pp. 1 ◽  
Author(s):  
Wang Qiquan
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Back Propagation ◽  
Warning System ◽  
Back Propagation Neural Network ◽  
Urban Road ◽  
Model Study

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 Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Ivana Sušanj ◽  
Nevenka Ožanić ◽  
Ivan Marović
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Small Scale ◽  
Ann Model ◽  
Data Value ◽  
The Mean ◽  
Artificial Neural

In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS) to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN) as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

Early-Warning Model for TECC Based on Gray Neural Network - Taking Laolongtou Scenic Area as an Example

2013 ◽  
Vol 397-400 ◽  
pp. 2435-2438
Author(s):  
Xiu Ping Yang ◽  
Er Chao Li
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Historical Data ◽  
Fuzzy Inference ◽  
Warning System ◽  
Forecasting Model ◽  
Error Signal ◽  
Single Feature ◽  
Warning Model

Based on fuzzy inference and gray neural network, indexes of early-warning system of carrying capacity in scenic spots is established and extract fuzzy rules based on historical data, simulate the early-warning system based on fuzzy inference, gray forecasting model is built for single feature index respectively, add a compensated error based on neural network. The prediction value equals to the output value of grey neural network model plus the compensated error signal. At last, takes Laolongtou scenic area as an example.

scholarly journals Early Warning System of Tennis Sports Injury Risk Based on Mobile Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yupeng Li ◽  
Kitak Kim ◽  
Yu Ding
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Injury Risk ◽  
Sports Injuries ◽  
Rbf Neural Network ◽  
Warning System ◽  
Sports Injury ◽  
Analysis Method ◽  
Tennis Players

In recent years, sports injuries in professional tennis players have gradually increased and sports injuries will break the sports training system and affect the long-term growth of new tennis players. Avoiding athlete injuries has become an important factor in improving training quality and game performance and ensuring the sustainable development of young tennis players’ competitiveness. Therefore, this article will use the RBF neural network algorithm and cluster analysis method to establish a tennis sports injury risk early warning model and finally establish a tennis sports injury risk early warning system so that tennis players can reduce their injuries. In this article, we use the questionnaire survey method, expert interview method, mathematical statistics method, and logical analysis method to investigate and analyze the results of training injuries of Chinese tennis players and coaches. The experimental results in this article show that among 48 tennis players of different ages, who are participating in formal training and tennis competitions, 15 young tennis players have been injured more than 6 times, accounting for 31.2% of the total; 20 have been injured 3 to 6 times, accounting for 41.7% of the total; 9 of them have been injured several times, accounting for 18.8% of the total; and 4 have been injured, accounting for 8.3% of the total. After using the tennis sports injury risk warning system based on the algorithm of RBF neural network in mobile computing, the tennis sports injury rate has dropped to 5%. It can be seen that the system has high feasibility and practicability.

Developing Strategies in the Sharing Economy

2022 ◽  
pp. 1224-1245
Author(s):  
Ramona Diana Leon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Early Warning ◽  
Early Warning System ◽  
Business Models ◽  
Warning System ◽  
Sharing Economy ◽  
Knowledge Based ◽  
Artificial Neural ◽  
The One

The sharing economy is challenging the traditional business models and strategies by encouraging collaboration, non-ownership, temporal access, and redistribution of goods and/or services. Within this framework, the current chapter aims to examine how managers influence, voluntarily or involuntarily, the reliability of a managerial early warning system, based on an artificial neural network. The analysis focuses on seven Romanian sustainable knowledge-based organizations and brings forward that managers tend to influence the results provided by a managerial early warning system based on artificial neural network, voluntarily and involuntarily. On the one hand, they are the ones who consciously decide which departments and persons are involved in establishing the structure of the managerial early warning system. On the other hand, they unconsciously influence the structure of the managerial early warning system through the authority they exercise during the managerial debate.

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