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Demand feature identifying for emergency goods under earthquake rescue logistics by vague set based bp neural network
Chang CG(常春光); Ma, Xiang; Song XY(宋晓宇); Kong FW(孔凡文)
Department工业信息学研究室
Conference Name2010 International Conference on Optoelectronics and Image Processing, ICOIP 2010
Conference DateNovember 11-12, 2010
Conference PlaceHaiko, China
Author of SourceHunan University of Science and Technology; Central South University; Intelligent Computation Technology and Automation Society; Changsha University of Science and Technology; InfoBeyond Technology LLC
Source PublicationProceedings - 2010 International Conference on Optoelectronics and Image Processing, ICOIP 2010
PublisherIEEE Computer Society
Publication PlacePiscataway, NJ
2010
Pages371-375
Indexed ByEI
EI Accession number20133016524155
Contribution Rank1
ISBN978-0-7695-4252-2
KeywordVague Set Bp Neural Network Membership Degree Function Earthquake
AbstractDuring the preliminary stage of rescue for earthquake disaster, some important information needs to be provided by identifying the key demand features such as quantity, necessary degree and urgent degree for emergency goods. To dispose uncertain information, especially unknown information for identifying above features, vague set is introduced into the conventional BP neural network. The relation among supportive degree, counteractive degree and unknown degree are analyzed. For disposing unknown information for vague set, the method for transforming vague set information into fuzzy set information is proposed. A series of rules for transforming vague values into fuzzy values are presented. Hereby, the fuzzy membership degree function is given. The structure of four-layer multi output VBP neural network is designed, and its implement steps are studied. To validate the validity of VBP neural network, it is applied to identify demand feature for emergency goods under earthquake. The result by VBP neural network is compared with those by conventional BP neural network and the VBP neural network which is based on conventional fuzzy membership degree transforming formula. The result shows that, the test precision by above VBP neural network is higher than those by the other two methods. As a novel learning method, VBP neural network is more suitable for training samples with uncertain information.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/19962
Collection工业信息学研究室
Corresponding AuthorChang CG(常春光)
Affiliation1.School of Management, Shenyang Jianzhu University, Shenyang, Liaoning, 11068, China
2.Key Laboratory of Industrial Informatics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, 110016, China
Recommended Citation
GB/T 7714
Chang CG,Ma, Xiang,Song XY,et al. Demand feature identifying for emergency goods under earthquake rescue logistics by vague set based bp neural network[C]//Hunan University of Science and Technology; Central South University; Intelligent Computation Technology and Automation Society; Changsha University of Science and Technology; InfoBeyond Technology LLC. Piscataway, NJ:IEEE Computer Society,2010:371-375.
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