{"title":"从经验数据估计Gielis超公式参数","authors":"Sudhanshu K. Mishra","doi":"10.2139/ssrn.905051","DOIUrl":null,"url":null,"abstract":"Johan Gielis showed that all closed curves might be considered as some sort of deformed ellipses. He gave a superformula to parameterize such shapes. In this study an attempt has been made to estimate the parameters of Gielis' superformula from empirical data. We use an optimum search algorithm on multi-modal surfaces - the Genetic Algorithm- to find the best fit. Randomly scattered starting points have been used for the search of an optimum solution. Some examples are based on simulated data, while the solution of a real problem also may be attempted. It has also been shown that the parameters of the superformula are not uniquely related to the data from which they are estimated. This lack of uniqueness may pose the problems of interpretation of parameters.","PeriodicalId":11044,"journal":{"name":"delete","volume":"31 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2006-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"On Estimation of the Parameters of Gielis Superformula from Empirical Data\",\"authors\":\"Sudhanshu K. Mishra\",\"doi\":\"10.2139/ssrn.905051\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Johan Gielis showed that all closed curves might be considered as some sort of deformed ellipses. He gave a superformula to parameterize such shapes. In this study an attempt has been made to estimate the parameters of Gielis' superformula from empirical data. We use an optimum search algorithm on multi-modal surfaces - the Genetic Algorithm- to find the best fit. Randomly scattered starting points have been used for the search of an optimum solution. Some examples are based on simulated data, while the solution of a real problem also may be attempted. It has also been shown that the parameters of the superformula are not uniquely related to the data from which they are estimated. This lack of uniqueness may pose the problems of interpretation of parameters.\",\"PeriodicalId\":11044,\"journal\":{\"name\":\"delete\",\"volume\":\"31 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2006-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"delete\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.905051\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"delete","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.905051","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7
摘要
Johan Gielis证明了所有的闭合曲线都可以被认为是某种变形的椭圆。他给出了一个超公式来参数化这些形状。本研究尝试从经验数据中估计吉利斯超公式的参数。我们使用了一种多模态曲面的最优搜索算法——遗传算法——来寻找最佳的拟合。随机分散的起始点被用来寻找最优解。一些例子是基于模拟数据的,同时也可以尝试解决实际问题。还表明,超公式的参数与估计它们所依据的数据并不是唯一相关的。这种唯一性的缺乏可能会造成参数解释的问题。
On Estimation of the Parameters of Gielis Superformula from Empirical Data
Johan Gielis showed that all closed curves might be considered as some sort of deformed ellipses. He gave a superformula to parameterize such shapes. In this study an attempt has been made to estimate the parameters of Gielis' superformula from empirical data. We use an optimum search algorithm on multi-modal surfaces - the Genetic Algorithm- to find the best fit. Randomly scattered starting points have been used for the search of an optimum solution. Some examples are based on simulated data, while the solution of a real problem also may be attempted. It has also been shown that the parameters of the superformula are not uniquely related to the data from which they are estimated. This lack of uniqueness may pose the problems of interpretation of parameters.