Listed Company Reorganization Risk Evaluation Based on Neural Network Model

2014 ◽  
pp. 241-250
Author(s):  
Wang Zuogong ◽  
Li Huiyang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Risk Evaluation ◽  
Listed Company

A NEURAL NETWORK MODEL FOR CREDIT RISK EVALUATION

2009 ◽  
Vol 19 (04) ◽  
pp. 285-294 ◽  
Author(s):  
ADNAN KHASHMAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Credit Risk ◽  
Network Model ◽  
Neural Network Model ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Learning Schemes

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

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.

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