{"title":"用粒子群算法辨识分数阶系统","authors":"Li Meng, Dongfeng Wang, P. Han","doi":"10.1109/ICMLC.2012.6359551","DOIUrl":null,"url":null,"abstract":"This paper presents the identification of fractional order system in frequency domain by using Particle Swarm Optimization (PSO) algorithm. PSO is extended to estimate the fractional derivative order. Meanwhile, recursive least squares algorithm is associated to calculate the denominator and numerator coefficients of transfer function. Simulation examples with noise-free and noisy data are given to verify the effectiveness of the method proposed in this paper.","PeriodicalId":128006,"journal":{"name":"2012 International Conference on Machine Learning and Cybernetics","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Identification of fractional order system using Particle Swarm Optimization\",\"authors\":\"Li Meng, Dongfeng Wang, P. Han\",\"doi\":\"10.1109/ICMLC.2012.6359551\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents the identification of fractional order system in frequency domain by using Particle Swarm Optimization (PSO) algorithm. PSO is extended to estimate the fractional derivative order. Meanwhile, recursive least squares algorithm is associated to calculate the denominator and numerator coefficients of transfer function. Simulation examples with noise-free and noisy data are given to verify the effectiveness of the method proposed in this paper.\",\"PeriodicalId\":128006,\"journal\":{\"name\":\"2012 International Conference on Machine Learning and Cybernetics\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-07-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 International Conference on Machine Learning and Cybernetics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMLC.2012.6359551\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 International Conference on Machine Learning and Cybernetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMLC.2012.6359551","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identification of fractional order system using Particle Swarm Optimization
This paper presents the identification of fractional order system in frequency domain by using Particle Swarm Optimization (PSO) algorithm. PSO is extended to estimate the fractional derivative order. Meanwhile, recursive least squares algorithm is associated to calculate the denominator and numerator coefficients of transfer function. Simulation examples with noise-free and noisy data are given to verify the effectiveness of the method proposed in this paper.