Research on Commercial Bank Credit Risk Evaluation Model Based on the Integration of the Probability Distribution Theory and the BP Neural Network Technology

2012 ◽  
Vol 4 (22) ◽  
pp. 115-128 ◽  
Author(s):  
Haihong Shao ◽  
Xiaofeng Ju ◽  
Chong Wu ◽  
Jianteng Xu ◽  
Maozhang Liu
Keyword(s):  
Neural Network ◽  
Probability Distribution ◽  
Commercial Bank ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Evaluation Model ◽  
Bank Credit ◽  
Network Technology ◽  
Neural Network Technology ◽  
Risk Evaluation Model

Power Grid Risk Evaluation Model of Empty Mine Mined-Out Area Based on BP Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 1710-1714
Author(s):  
Guo Feng Yang ◽  
Jia Kui Zhao ◽  
Ting Shun Li ◽  
Jing Zhou
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Preventive Measures ◽  
Evaluation Model ◽  
Factors Associated ◽  
Bp Neural Network Model ◽  
Risk Evaluation Model

The risk evaluation of empty mine mined-out area is of great significance to the security and stability of the power facilities. But influence evaluation of empty mine mined-out area factor multitudinous, this paper selected seven factors associated with the system. Based on the principle of BP neural network, we build a 3 layer BP neural network model suitable for grid risk evaluation. The BP neural network model was trained by collected samples of empty mine mined-out area and the logical parameters of BP neural network were acquired and tested by the testing samples for accuracy, and finally we proposes preventive measures based on the evaluation.

The Risk Evaluation Model of Construction Project Contract Based on BP Neural Network

2013 ◽  
Vol 357-360 ◽  
pp. 2304-2307
Author(s):  
Hua Liu ◽  
Jun Fang Yang ◽  
Zhi Yuan Zhang
Keyword(s):  
Neural Network ◽  
Risk Management ◽  
Bp Neural Network ◽  
Risk Evaluation ◽  
Evaluation Model ◽  
Neural Network Method ◽  
Network Method ◽  
Indicator Model ◽  
Current Risk ◽  
Risk Evaluation Model

According to the current risk management of construction contract in China, put forward the idea of using neural network method to evaluate the risk in construction contract. Designed a comprehensive multi-indicator model for evaluating the construction contract risk based on BP Neural Network. This model can be used for simulating and evaluating the risk in construction contract in future. It has been proved that the desired results can be achieved by using this model.

Applied Research Of BP Neural Network In Earthquake Prediction

2012 ◽  
Vol 204-208 ◽  
pp. 2449-2454 ◽  
Author(s):  
Wu Sheng Hu ◽  
Hong Lin Nie ◽  
Hao Wang
Keyword(s):  
Neural Network ◽  
Earthquake Prediction ◽  
Bp Neural Network ◽  
Integrated Model ◽  
Earthquake Magnitude ◽  
Linear Regression Method ◽  
Network Technology ◽  
Short Term ◽  
Non Linear ◽  
Neural Network Technology

Nowadays, earthquake prediction is still a worldwide scientific problem, especially the prediction for short-term and imminent earthquake has no substantial breakthroughs. BP neural network technology has a strong non-linear mapping function which could better reflect the strong non-linear relationship between earthquake precursors and the time and the magnitude of a potential earthquake. In this paper, we selected the region of Beijing as the research area and 3 months as the prediction period. Based on BP neural network and integrated with the conventional linear regression method, a regional short-term integrated model was established, which gives the quantitative prediction for the earthquake magnitude. The results show that the earthquake magnitude prediction RMSE (root mean square error) of the integrated model reaches ± 0.28 Ms. Compared with conventional methods, the integrated model improves significantly. The new model has a good prospect to use BP neural network technology for earthquake prediction.

Prediction of EVA Plastic's Expansion Ratio Based on BP Neural Network

2013 ◽  
Vol 364 ◽  
pp. 529-533
Author(s):  
Lai Fa Zhu ◽  
Bin Liu ◽  
Jian Wen Xu
Keyword(s):  
Neural Network ◽  
Experimental Design ◽  
Bp Neural Network ◽  
Thin Sheet ◽  
Injection Pressure ◽  
Expansion Ratio ◽  
Network Technology ◽  
Sample Data ◽  
Different Diameters ◽  
Neural Network Technology

Combined with Taguchi method of experimental design and BP neural network technology, select process parameters including molding temperature, molding time and injection pressure, Taguchi experiments are respectively arranged for different diameters of sphere and cylinder, different side lengths of tri-prism and quadrangular prism, different thicknesses of thin sheet without hole and with hole etc. molds, then these experiment results are used as neural network sample data, and expansion ratio of EVA plastic can be predicted more accurately after neural network is trained.

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