G. Constantinescu, C. Strîmbu, M. Pearsica, L. Miron
{"title":"A Method for Periodical Phenomena Analysis","authors":"G. Constantinescu, C. Strîmbu, M. Pearsica, L. Miron","doi":"10.1109/SOFA.2007.4318319","DOIUrl":null,"url":null,"abstract":"This paper is proposing a statistical method, useful for analyzing periodical phenomena whose equations are impossible to be solved analytically. The input data consist in a collection of tabled functions, (numerically determined), named further database. Least square method based algorithms, presented in the first part of the paper, are applied to this database. First, a trigonometric regression algorithm will find the approximating Fourier coefficients. Finally a multiple regression algorithm to fit a polynomial type function is introduced. Its input data are the Fourier coefficients, the Fourier analysis results, or whatever collection of experimental data corresponding to a set of variables. The final part of the paper is dedicated to an example, illustrative for these.","PeriodicalId":205589,"journal":{"name":"2007 2nd International Workshop on Soft Computing Applications","volume":"655 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2007-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2007 2nd International Workshop on Soft Computing Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SOFA.2007.4318319","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper is proposing a statistical method, useful for analyzing periodical phenomena whose equations are impossible to be solved analytically. The input data consist in a collection of tabled functions, (numerically determined), named further database. Least square method based algorithms, presented in the first part of the paper, are applied to this database. First, a trigonometric regression algorithm will find the approximating Fourier coefficients. Finally a multiple regression algorithm to fit a polynomial type function is introduced. Its input data are the Fourier coefficients, the Fourier analysis results, or whatever collection of experimental data corresponding to a set of variables. The final part of the paper is dedicated to an example, illustrative for these.