DocumentCode :
2658097
Title :
Application of rough sets and artificial neural network in core enterprise performance prediction
Author :
Jian, Hu ; Chengdong, Shi
Author_Institution :
Sch. of Electr. & Electron. Eng., Shandong Univ. of Technol., Zibo
fYear :
2008
fDate :
16-18 July 2008
Firstpage :
748
Lastpage :
752
Abstract :
A predication model of core enterprise performance was proposed based on rough sets and artificial neural network from knowledge discovery and data mining perspective at first. Then, the calculation and analysis process of the model were given and discussed. The performance decision-making table and discernable matrix were designed, and the artificial neural network and back propagation algorithm (BP network) were put forward. Finally, the model was applied into a practical prediction example study. After the balanced scorecard index system was reduced and the reduction index was input to the artificial neural network for intelligent training, the predicted sample was input to the trained network, the prediction value of the core enterprise performance was gained. The prediction result is consistent with the actual result.
Keywords :
backpropagation; data mining; matrix algebra; neural nets; organisational aspects; rough set theory; BP network; artificial neural network; backpropagation algorithm; balanced scorecard index system; core enterprise performance prediction; data mining perspective; discernable matrix; knowledge discovery; performance decision-making; rough sets; Artificial intelligence; Artificial neural networks; Biological system modeling; Decision making; Intelligent networks; Multi-layer neural network; Neural networks; Predictive models; Rough sets; Supply chains; Artificial neural network; Core enterprise; Discernable matrix; Performance prediction model; Rough sets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference, 2008. CCC 2008. 27th Chinese
Conference_Location :
Kunming
Print_ISBN :
978-7-900719-70-6
Electronic_ISBN :
978-7-900719-70-6
Type :
conf
DOI :
10.1109/CHICC.2008.4605035
Filename :
4605035
Link To Document :
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