DocumentCode
2815549
Title
Notice of Retraction
Prediction and assessment of agricultural modernization level based on topsis and artificial neural network
Author
Qi Wang ; Haihu Ma ; Xiaodan Wang
Author_Institution
Coll. of Life & Environ. Sci., Wenzhou Univ., Wenzhou, China
Volume
2
fYear
2010
fDate
22-24 Oct. 2010
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.
Agricultural modernization is crucial for the economic development of agriculture. Technique for order preference by similarity to an ideal solution (Topsis) and artificial neural network (ANN) were employed to evaluate and predict agricultural modernization level. A three-layer ANN models were developed to predict agricultural modernization level. The models were compared using the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). The 5-4-1 ANN model with standardization transformation was the best model. Topsis and ANN are useful tools to evaluate agricultural modernization level and to provide policy proposals for more efficient decision-making for the local government.
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.
Agricultural modernization is crucial for the economic development of agriculture. Technique for order preference by similarity to an ideal solution (Topsis) and artificial neural network (ANN) were employed to evaluate and predict agricultural modernization level. A three-layer ANN models were developed to predict agricultural modernization level. The models were compared using the mean absolute error (MAE), mean absolute percentage error (MAPE) and root mean square error (RMSE). The 5-4-1 ANN model with standardization transformation was the best model. Topsis and ANN are useful tools to evaluate agricultural modernization level and to provide policy proposals for more efficient decision-making for the local government.
Keywords
agriculture; decision making; local government; mean square error methods; neural nets; socio-economic effects; standardisation; Topsis; agricultural modernization level; artificial neural network; decision-making; economic development; ideal solution; local government; mean absolute error; mean absolute percentage error; root mean square error; standardization transformation; Artificial neural networks; Biological system modeling; Indexes; Predictive models; Standardization; Thermal analysis; agricultural modernization level; artificial neural network; grey relational analysis; root mean square error; topsis;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location
Taiyuan
Print_ISBN
978-1-4244-7235-2
Type
conf
DOI
10.1109/ICCASM.2010.5619382
Filename
5619382
Link To Document