DocumentCode
2236349
Title
Notice of Retraction
Mechanism of N-addition Grey Neural Network Model and its Application
Author
Cuifeng Li
Author_Institution
Electr. & Mech. Eng. Coll., Zhejiang Bus. Technol. Inst., Ningbo, China
fYear
2009
fDate
24-25 April 2009
Firstpage
372
Lastpage
375
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The modeling precision will be affected by the randomness inherent in the data when neural network approach is used for the model, so grey theory is introduced into the neural network based on grey accumulated generating operation can reduce randomness of the data, N-addition grey neural network model is proposed. The model is successfully used to build model of per-grain output. The practical application results show the effectiveness of the proposed approach. The practical example shows that the model proposed by this paper is definite in concept, convenient in calculation, good in fitting and precise in prediction, thus this method improves the precision of the GM(1.1) model and enlarges its application scope.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
The modeling precision will be affected by the randomness inherent in the data when neural network approach is used for the model, so grey theory is introduced into the neural network based on grey accumulated generating operation can reduce randomness of the data, N-addition grey neural network model is proposed. The model is successfully used to build model of per-grain output. The practical application results show the effectiveness of the proposed approach. The practical example shows that the model proposed by this paper is definite in concept, convenient in calculation, good in fitting and precise in prediction, thus this method improves the precision of the GM(1.1) model and enlarges its application scope.
Keywords
grey systems; neural nets; N-addition model; grey theory; neural network model; Computer errors; Educational institutions; Error correction; Fluctuations; Information systems; Mechanical engineering; Neural networks; Neurofeedback; Predictive models; Uncertainty; connection weights; grey model; map; neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Industrial and Information Systems, 2009. IIS '09. International Conference on
Conference_Location
Haikou
Print_ISBN
978-0-7695-3618-7
Type
conf
DOI
10.1109/IIS.2009.104
Filename
5116376
Link To Document