Application of the Back Propagation Neural Network Model on the Supplier Evaluation in E-Manufacturing Environment

2009 ◽  
Vol 16-19 ◽  
pp. 174-177
Author(s):  
Jian Chen ◽  
Ming Hong Wu ◽  
Ilias Oraifige

The supplier evaluation is a key section of the intelligent internet supplier selection & evaluation system. The model used for supplier evaluation is Back Propagation Neural Network model which is introduced in the paper. The paper started with the brief introduction of the intelligent internet supplier selection & evaluation system. It provides a outline of the research project and then it concentrated to introduce the application of the BP NN model for supplier evaluation. The application introduced in the paper will include the design of the BP NN model, Training of the BP NN model and test results.

Author(s):  
Venkata R. Duddu ◽  
Srinivas S. Pulugurtha ◽  
Ajinkya S. Mane ◽  
Christopher Godfrey

2009 ◽  
Vol 19 (04) ◽  
pp. 285-294 ◽  
Author(s):  
ADNAN KHASHMAN

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.


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