DocumentCode
526421
Title
Notice of Retraction
Application research on genetic neural network for water quality comprehensive evaluation
Author
Lian Zhang ; Wen-juan Li ; Fang-fang Zhang ; Wei Lai
Author_Institution
Sch. of Electron. Inf. & Autom., Chongqing Univ. of Technol., Chongqing, China
Volume
8
fYear
2010
fDate
9-11 July 2010
Firstpage
636
Lastpage
640
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.
In order to overcome the shortcomings of BP neural network, the golden section theory was used to get the reasonable number of Back Propagation (BP) neural network´s hidden nodes. By using Genetic Algorithm (GA) to optimize the initial weights and threshold value of BP neural network, the network converged quickly and the recognition precision was increased. The GA-BP neural network model was utilized to evaluate the water quality degree of 5 rivers in Xindu area of Chengdu, the evaluation results were compared with the results of single factor method and comprehensive pollution evaluation method. It indicated that the GA-BP neural network model has practicability, objectivity and generality in the application of water quality comprehensive evaluation.
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.
In order to overcome the shortcomings of BP neural network, the golden section theory was used to get the reasonable number of Back Propagation (BP) neural network´s hidden nodes. By using Genetic Algorithm (GA) to optimize the initial weights and threshold value of BP neural network, the network converged quickly and the recognition precision was increased. The GA-BP neural network model was utilized to evaluate the water quality degree of 5 rivers in Xindu area of Chengdu, the evaluation results were compared with the results of single factor method and comprehensive pollution evaluation method. It indicated that the GA-BP neural network model has practicability, objectivity and generality in the application of water quality comprehensive evaluation.
Keywords
backpropagation; environmental science computing; genetic algorithms; geophysics computing; neural nets; rivers; water quality; Chengdu; Xindu area; backpropagation neural network; genetic algorithm; genetic neural network; golden section theory; hidden node; rivers; threshold value; water quality evaluation; Board of Directors; Petroleum; Rivers; Robustness; BP Neural Network; Genetic Algorithm; Golden Section Theory; Water Quality Evaluation;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5563997
Filename
5563997
Link To Document