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《Journal of Chongqing University of Technology(Natural Science)》 2015-11
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Improved EMD Method Based on Genetic Algorithm and Support Vector Machine

ZHANG Jing;YANG Chao;ZENG Jian-mei;LI Guan-di;College of Electronic Engineering and Automation,Chongqing University of Technology;  
Aiming at the end effects problem of EMD( empirical mode decomposition),this paper put forward an end effects suppression method based on genetic algorithm and support vector machine.The method used support vector machines for extending both ends of the time series and introduced genetic algorithm which made the extension more reasonable and the decomposition results more satisfactory. By studying the sequence of simulation and pulse signal,the result shows that this method can reduce the end effect effectively.
【Fund】: 国家自然科学基金资助项目(11242006);; 重庆市科技创新能力建设计划项目(CSTC2013kjrc-tdjs40012)
【CateGory Index】: TN911.6;TP18
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