Based on factor analysis of BP neural network China logistics operation performance of listed companies

2010 ◽  
Author(s):  
Liming Wu ◽  
Huipeng Gan
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Bp Neural Network ◽  
Listed Companies ◽  
Operation Performance

An Improved BP Neural Network Algorithm Based on Factor Analysis

2010 ◽  
Vol 5 (4) ◽  
pp. 103-108 ◽  
Author(s):  
Shifei Ding ◽  
Weikuan Jia ◽  
Chunyang Su ◽  
Xiaoliang Liu ◽  
Jinrong Chen
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Bp Neural Network ◽  
Network Algorithm ◽  
Neural Network Algorithm

The Positive Analysis of the Impact on Operation Performance of Non-Agricultural Business for Agricultural Listed Companies

2012 ◽  
Vol 459 ◽  
pp. 137-140
Author(s):  
Qin Chen ◽  
Xiao Mei Zhang
Keyword(s):  
Factor Analysis ◽  
Empirical Analysis ◽  
Listed Companies ◽  
Operation Performance ◽  
Positive Analysis ◽  
Agricultural Business ◽  
The Impact

There are some significant problems in the process of development of agricultural listed companies ,especially is the existance of non-agricultural business. The paper uses the Factor Analysis to analyse the influence on operation performance of non-agricultural business for agricultural Listed Companies in china.According to empirical analysis,the conclusion is that: business on the non-agricultural term cut down the performances of the Agricultural Listed Companies,agricultural listed companies should not carry on the expansion on aspects of non-agricultural

Study on Cost Estimate at Product Design Stage Based on Factor Analysis and Neural Network

2011 ◽  
Vol 403-408 ◽  
pp. 1781-1785 ◽  
Author(s):  
Yi Zheng ◽  
Ming Hua Wang ◽  
Tao Yang
Keyword(s):  
Neural Network ◽  
Factor Analysis ◽  
Product Design ◽  
Bp Neural Network ◽  
Concurrent Engineering ◽  
Cost Control ◽  
New Products ◽  
Cost Estimate ◽  
Design Stage ◽  
The Cost

The cost estimate plays an important role in cost control and developing new products at the design stage. To improve the accuracy of cost estimate, we extract the feature parameters using the theory of concurrent engineering and factor analysis. Then we propose DCEM that is the model of cost estimate based on factor analysis and BP neural network. The model not only simplifies the input of BP neural network, but also avoids the coupling among the input parameters. The result shows that the model’s performance is stable and it can estimate the cost more accurate at the early product design stage.

SOM-BP Neural Network-Based Financial Early-Warning for Listed Companies

2016 ◽  
Vol 13 (10) ◽  
pp. 6860-6866 ◽  
Author(s):  
Yu Hong ◽  
Wei Sun ◽  
Bai Qianling ◽  
Xiaowei Mu
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Bp Neural Network ◽  
Weight Vector ◽  
Listed Companies ◽  
Financial Risks ◽  
Short Term ◽  
Monitoring Model ◽  
Training Samples ◽  
Short Term Forecasting

To prevent and reduce corporate financial risks, this paper builds a financial early-warning model for listed companies based on a combination of SOM and BP neural networks focusing on short-term financial forecasting and monitoring. Firstly, SOM network is utilized to allow self-modification of unit connection weights according to the feature information of input data and enable the weight vector distribution to be similar to the distribution of sample data, thereby obtaining relatively optimal training samples among all training samples. Then, a short-term financial early-warning monitoring model is created through iterative BP training with the relatively optimal samples extracted as the input information of the BP neural network model. The results show that the proposed financial earlywarning system has higher recognition accuracy than the direct use of Logistic model, BP model or SVM model in term of short-term forecasting and monitoring. Furthermore, our model requires less amount of data while ensuring the validity. Therefore, it can monitor financial crises in real time for listed companies, so as to effectively prevent and resolve their financial risks and crises.

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