{"title":"Analysis of Overlapping Voltammograms of Nitrophenols Combining Genetic Algorithms and Support Vector Machines","authors":"G. Ling, Ren Shou-xin","doi":"10.1109/ICICTA.2015.53","DOIUrl":null,"url":null,"abstract":"This paper suggests a novel method named GA-LSSVM, combines genetic algorithms (GA) and least squares support vector machines (LS-SVM) techniques to provide a powerful model for improving the regression quality and to enhance the ability to extract characteristic information. Simultaneous differential pulse voltammetric multi-component determination of o-nitro phenol, m-nitro phenol and pnitrophenol was conducted for the first time by using the proposed method. The LS-SVM technique broadens the application of SVM by reducing the computational complexity since only the solution of a set of linear equations is required instead of a quadratic programming problem. Thus, LS-SVM has the capability of solving linear and nonlinear multivariate calibrations in a relatively fast way. Genetic algorithms (GA) introduced are probabilistic optimization techniques based on natural evolution and genetics and Darwin's theory of survival of the best. The GA-LS-SVM method is proven to be successful even when severe overlap of voltammograms existed.","PeriodicalId":231694,"journal":{"name":"2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICICTA.2015.53","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 suggests a novel method named GA-LSSVM, combines genetic algorithms (GA) and least squares support vector machines (LS-SVM) techniques to provide a powerful model for improving the regression quality and to enhance the ability to extract characteristic information. Simultaneous differential pulse voltammetric multi-component determination of o-nitro phenol, m-nitro phenol and pnitrophenol was conducted for the first time by using the proposed method. The LS-SVM technique broadens the application of SVM by reducing the computational complexity since only the solution of a set of linear equations is required instead of a quadratic programming problem. Thus, LS-SVM has the capability of solving linear and nonlinear multivariate calibrations in a relatively fast way. Genetic algorithms (GA) introduced are probabilistic optimization techniques based on natural evolution and genetics and Darwin's theory of survival of the best. The GA-LS-SVM method is proven to be successful even when severe overlap of voltammograms existed.