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Articles by Weiping Deng
Total Records ( 2 ) for Weiping Deng
  Weiping Deng , Jianzhong Zhou , Qiang Zou , Yongchuan Zhang and Weihua Hua
  Flood disasters always occur occasionally and unpredictably. In order to reflect the fuzzy and occasional characters of flood, furthermore evaluate flood disaster accurately and timely, an evaluation approach based on improved cloud model is proposed in this study. Cloud model is a transformation tool between quantity and quality. It can not only qualitatively analyze the fuzzy character of assessment indexes but also can reflect the randomness of flood disaster. Moreover it can quantitatively evaluate the disaster. The certainty degree method is also used to analysis and solves the problem of failure caused by the maximum membership principle in cloud model. The revised method improves the experiment result. Compared with the assessment result by the grey clustering method and the fuzzy method, the amended cloud model method is confirmed to be a reliable method for rapid assessment.
  Weiping Deng , Jianzhong Zhou , Qiang Zou , Jian Xiao , Yongchuan Zhang and Weihua Hua
  Support vector machine is adopted in this paper to construct flood disaster evaluation model, which can be indicated as a comprehensively nonlinear classification issue. In this article, kernel function of SVM is optimized by kernel transformation and kernel parameters optimization. In order to discriminate the flood disaster evaluation indexes and really reflect their classification contributions, kernel function is weighted to promote classification performance and reduce the error influence by weak features. Further more, after analyzing the over learning issue of traditional grid search, an improved grid search is proposed to optimize the kernel parameters. The new search method illustrates it is more reasonable to make second search around some suboptimal solutions. By this search method, the final solution obtains high accuracy in testing samples and reduces the value of penalty factor. The experiment results show the promotion of classification precision by the dual optimization model and identify it could be a good choice for other classification issues.
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