Method for Evaluation of Railway Dynamic Safety Based on 5M Model and Neural Network Algorithm

2015 ◽  
Vol 743 ◽  
pp. 445-449 ◽  
Author(s):  
G. Wang ◽  
Xiao Qing Zeng ◽  
J. Li
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Safety Assessment ◽  
Dynamic Assessment ◽  
Development Trend ◽  
Rail Transit ◽  
Network Algorithm ◽  
Railway Safety ◽  
Neural Network Algorithm ◽  
The Development Trend

Traditional risk assessment methods, mainly static analysis, can’t anticipate the development trend and consequences of accidents. This paper firstly presents 5M safety model, combined with the characteristics of the rail transit safety assessment, including complexity, dynamic, ambiguity, etc. By using neural network algorithm, this paper proposes a method of railway safety dynamic assessment. Finally, the method is validated by comparing the output value of the model and the veritable value. The result indicated that it has advantages of real time and predictability.

Enterprise supply chain risk assessment based on improved neural network algorithm and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Shaoqin Lu
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Risk Assessment ◽  
Supply Chain ◽  
Semantic Network ◽  
System Structure ◽  
Supply Chain Risk ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Risk Knowledge

It is of practical significance to study the decision-making subject in the supply chain under the influence of risk aversion to make a decision and make the supply chain compete in an orderly market environment. In order to improve the effect of enterprise supply chain risk assessment, this paper improves the traditional neural network algorithm, combines machine learning methods and supply chain risk assessment time requirements to set system function modules, and builds the overall system structure. Considering the multiple relationship attributes of supply chain risk knowledge, this paper uses a multi-element semantic network to represent the network structure of supply chain risk knowledge, and proposes a multi-level inventory control modelThis is based on the inventory of the coordination center and other retailers’ procurement/relocation strategy models. After building the model, this paper designs a simulation test to verify and analyze the model performance. The research results show that the model proposed in this paper has a certain effect.

A NEURAL NETWORK ALGORITHM FOR DENSITY MEASUREMENT OF MULTIPHASE FLOW

2012 ◽  
Vol 24 (2) ◽  
pp. 89-103 ◽  
Author(s):  
Nabeel Al-Rawahi ◽  
Mahmoud Meribout ◽  
Ahmed Al-Naamany ◽  
Ali Al-Bimani ◽  
Adel Meribout
Keyword(s):  
Neural Network ◽  
Multiphase Flow ◽  
Density Measurement ◽  
Network Algorithm ◽  
Neural Network Algorithm

Logistics demand forecasting model based on improved neural network algorithm

2020 ◽  
pp. 1-11
Author(s):  
Hongjiang Ma ◽  
Xu Luo
Keyword(s):  
Neural Network ◽  
Influencing Factor ◽  
Demand Forecasting ◽  
Forecasting Model ◽  
Logistics System ◽  
Model Based ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Demand Forecasting Model ◽  
Logistics Demand

The irrationality between the procurement and distribution of the logistics system increases unnecessary circulation links and greatly reduces logistics efficiency, which not only causes a waste of transportation resources, but also increases logistics costs. In order to improve the operation efficiency of the logistics system, based on the improved neural network algorithm, this paper combines the logistic regression algorithm to construct a logistics demand forecasting model based on the improved neural network algorithm. Moreover, according to the characteristics of the complexity of the data in the data mining task itself, this article optimizes the ladder network structure, and combines its supervisory decision-making part with the shallow network to make the model more suitable for logistics demand forecasting. In addition, this paper analyzes the performance of the model based on examples and uses the grey relational analysis method to give the degree of correlation between each influencing factor and logistics demand. The research results show that the model constructed in this paper is reasonable and can be analyzed from a practical perspective.

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