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The Technology Of Immunity Detection Based On Cloud Model

Posted on:2015-11-07Degree:MasterType:Thesis
Country:ChinaCandidate:S QinFull Text:PDF
GTID:2348330518972136Subject:Pattern Recognition and Intelligent Systems
Abstract/Summary:PDF Full Text Request
Based on the excellent characteristics of the cloud model in dealing with fuzzy and random phenomenon, this article studies the cloud model theory and the theory of the immune detection algorithms in depth by referring to domestic and foreign information. To further, the cloud model theory has applied to immune detection algorithm. Improve backward cloud generation algorithm to enhance the accuracy of the algorithm. The use of cloud model theory generates detector set, which has improved adaptive of the detector set.The design of immune detection algorithm based on cloud model theory has improved the detection efficiency. Finally,the vaccine theory is analyzed; the cloud vaccine operator is designed, and added it to detector set, which has improved the second response time of detection.The main work of this paper:1. This paper has shown that the definition and the properties of the normal cloud model,as well as forward cloud generation algorithm and backward cloud generation algorithm. It emphatically analyzes the backward cloud generation algorithm. For the curve fitting of backward cloud generation algorithm of initial selection is random, so that the experimental results is unstable, therefore, it has been proposed a backward cloud improved algorithm.With the correlation function, the selection of fitting initial has optimized, which improves the stability of the algorithm and the accuracy of the fitting parameters. It is simulated for comparison by MATLAB.2. The research of randomness and fuzziness and association of them in cloud model,and studying the artificial immune detection algorithm deeply, combined with the detector generation method of the negative selection algorithm, using cloud model theory generates the detector set, this generation method of detector set is adaptive, and verify the coverage of this detector set.3. Based on the depth studying of adjustable fuzzy matching negative selection algorithm. Using the cloud model theory to solve the problem of uncertainty in the immune detection algorithm, by defining antigen class attribute using the cloud model, using multiple detectors detect the same uncertain antigen x, and then adopting backward cloud generates algorithm statistics experimental results ,and then analysis the results, Judging the class attribute of the antigen jointly, and the cloud model detection algorithm applied to check the text files and C language target files, for simulation. To carry out simulation, improve the efficiency of detection.4. To further improve the efficiency and accuracy of detection, in the latter detection, it extracts the vaccine operator, and using randomness and fuzziness on cloud model characteristic generates cloud vaccine operator, which was added to the detector set, the detector set has certain relevance. When the antigen is detected, using the cloud vaccines operator of targeted detected, it can shorten the second response time and improve the detection efficiency and validation of analytical.
Keywords/Search Tags:Cloud model, Immune detection, Detector set, Cloud vaccine operator
PDF Full Text Request
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