{"title":"分数阶傅里叶变换中改进的混沌优化方法","authors":"Hongkai Wei, Pingbo Wang, Zhiming Cai, Yinfeng Fu","doi":"10.1109/ICSAP.2010.80","DOIUrl":null,"url":null,"abstract":"In order to overcome the inefficiency shortcoming of traditional step-based searching method for extremum seeking in two-dimensional fractional Fourier domain, the chaos optimization method is introduced and applied successfully in fractional Fourier transform. To accelerate the convergence further, two improved chaos optimization methods are proposed. The performances of the proposed optimization methods are verified by comparing with step-based method and other intelligent optimization methods such as genetic algorithms, continuous ant colony algorithm and particle swarm optimization based on simulation. Results show that the second presented chaos optimization algorithm is more preferable considering computation efficiency, precision and resolution.","PeriodicalId":303366,"journal":{"name":"2010 International Conference on Signal Acquisition and Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Improved Chaos Optimization Method in the Fractional Fourier Transform\",\"authors\":\"Hongkai Wei, Pingbo Wang, Zhiming Cai, Yinfeng Fu\",\"doi\":\"10.1109/ICSAP.2010.80\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In order to overcome the inefficiency shortcoming of traditional step-based searching method for extremum seeking in two-dimensional fractional Fourier domain, the chaos optimization method is introduced and applied successfully in fractional Fourier transform. To accelerate the convergence further, two improved chaos optimization methods are proposed. The performances of the proposed optimization methods are verified by comparing with step-based method and other intelligent optimization methods such as genetic algorithms, continuous ant colony algorithm and particle swarm optimization based on simulation. Results show that the second presented chaos optimization algorithm is more preferable considering computation efficiency, precision and resolution.\",\"PeriodicalId\":303366,\"journal\":{\"name\":\"2010 International Conference on Signal Acquisition and Processing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-02-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 International Conference on Signal Acquisition and Processing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICSAP.2010.80\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 International Conference on Signal Acquisition and Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSAP.2010.80","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Improved Chaos Optimization Method in the Fractional Fourier Transform
In order to overcome the inefficiency shortcoming of traditional step-based searching method for extremum seeking in two-dimensional fractional Fourier domain, the chaos optimization method is introduced and applied successfully in fractional Fourier transform. To accelerate the convergence further, two improved chaos optimization methods are proposed. The performances of the proposed optimization methods are verified by comparing with step-based method and other intelligent optimization methods such as genetic algorithms, continuous ant colony algorithm and particle swarm optimization based on simulation. Results show that the second presented chaos optimization algorithm is more preferable considering computation efficiency, precision and resolution.