{"title":"基于遗传规划的混沌时间序列建模。","authors":"Wei Zhang, Zhi-ming Wu, Gen-ke Yang","doi":"10.1631/jzus.2004.1432","DOIUrl":null,"url":null,"abstract":"<p><p>This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.</p>","PeriodicalId":85042,"journal":{"name":"Journal of Zhejiang University. Science","volume":"5 11","pages":"1432-9"},"PeriodicalIF":0.0000,"publicationDate":"2004-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1631/jzus.2004.1432","citationCount":"20","resultStr":"{\"title\":\"Genetic programming-based chaotic time series modeling.\",\"authors\":\"Wei Zhang, Zhi-ming Wu, Gen-ke Yang\",\"doi\":\"10.1631/jzus.2004.1432\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.</p>\",\"PeriodicalId\":85042,\"journal\":{\"name\":\"Journal of Zhejiang University. Science\",\"volume\":\"5 11\",\"pages\":\"1432-9\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1631/jzus.2004.1432\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Zhejiang University. Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1631/jzus.2004.1432\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Zhejiang University. Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1631/jzus.2004.1432","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genetic programming-based chaotic time series modeling.
This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the Particle Swarm Optimization (PSO) algorithm is used for Nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.
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