{"title":"阶梯结构gff模型的遗传算法优化","authors":"J. B. Machado","doi":"10.1109/SysCon.2014.6819287","DOIUrl":null,"url":null,"abstract":"A new technique for systems identification using ladder-structured generalized orthonormal basis function model is presented. In this approach the model poles and the number of functions are optimized using a genetic algorithm. A fitness function based on the Akaike information criterion considering model accuracy and model parsimony provides optimal number of functions and poles of the system model. Simulated and a real examples illustrate the performance of the proposed technique.","PeriodicalId":72236,"journal":{"name":"Annual IEEE Systems Conference. IEEE Systems Conference","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"GA optimization of ladder-structured GOBF models\",\"authors\":\"J. B. Machado\",\"doi\":\"10.1109/SysCon.2014.6819287\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A new technique for systems identification using ladder-structured generalized orthonormal basis function model is presented. In this approach the model poles and the number of functions are optimized using a genetic algorithm. A fitness function based on the Akaike information criterion considering model accuracy and model parsimony provides optimal number of functions and poles of the system model. Simulated and a real examples illustrate the performance of the proposed technique.\",\"PeriodicalId\":72236,\"journal\":{\"name\":\"Annual IEEE Systems Conference. IEEE Systems Conference\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual IEEE Systems Conference. IEEE Systems Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SysCon.2014.6819287\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual IEEE Systems Conference. IEEE Systems Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SysCon.2014.6819287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A new technique for systems identification using ladder-structured generalized orthonormal basis function model is presented. In this approach the model poles and the number of functions are optimized using a genetic algorithm. A fitness function based on the Akaike information criterion considering model accuracy and model parsimony provides optimal number of functions and poles of the system model. Simulated and a real examples illustrate the performance of the proposed technique.