DocumentCode
3442966
Title
Notice of Retraction
Research on damage level assessment model based on CGA-SVM
Author
Zhifeng You ; Quan Shi ; Xianglong Ni ; Ning Ding
Author_Institution
Manage. Eng. Dept., Mech. Eng. Coll., Shijiazhuang, China
fYear
2013
fDate
15-18 July 2013
Firstpage
1906
Lastpage
1909
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 battlefield damage level assessment is different from other assessment problems because of its characteristics such as small sample, multiple influence factors and high dimension. Thereby, the assessment methods like Bayesian Net (BN), Neutral Net (NN), and multiple indexes comprehensive method can´t be used in this domain. So, a new method must be introduced. Then, Support Vector Mechanism (SVM) which can deal with the small sample, multi-indexes and high dimension events is seemed suitable to this problem. To reduce the complexity of the SVM model, Gauss formula is choose as the kernel function of SVM. The parameter of SVM is optimized by the Cloud Genetic Algorithm (CGA) which can accelerate the GA´s searching rate and keep its search randomly to overcome falling into local optimal solution. The model has been proved to be efficient for damage level assessment via a case study.
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 battlefield damage level assessment is different from other assessment problems because of its characteristics such as small sample, multiple influence factors and high dimension. Thereby, the assessment methods like Bayesian Net (BN), Neutral Net (NN), and multiple indexes comprehensive method can´t be used in this domain. So, a new method must be introduced. Then, Support Vector Mechanism (SVM) which can deal with the small sample, multi-indexes and high dimension events is seemed suitable to this problem. To reduce the complexity of the SVM model, Gauss formula is choose as the kernel function of SVM. The parameter of SVM is optimized by the Cloud Genetic Algorithm (CGA) which can accelerate the GA´s searching rate and keep its search randomly to overcome falling into local optimal solution. The model has been proved to be efficient for damage level assessment via a case study.
Keywords
genetic algorithms; military computing; military equipment; search problems; support vector machines; BN; Bayesian network; CGA-SVM; GA searching rate; Gauss formula; battlefield damage level assessment model; cloud genetic algorithm; high dimension events; kernel function; local optimal solution; multiple indexes comprehensive method; neutral network; support vector mechanism; Educational institutions; Generators; Genetic algorithms; Indexes; Kernel; Optimization; Support vector machines; cloud genetic algorithm; code scheme; damage level assessment; support vector mechanism;
fLanguage
English
Publisher
ieee
Conference_Titel
Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE), 2013 International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4799-1014-4
Type
conf
DOI
10.1109/QR2MSE.2013.6625952
Filename
6625952
Link To Document