Analysis of Overlapping Voltammograms of Nitrophenols Combining Genetic Algorithms and Support Vector Machines

G. Ling, Ren Shou-xin
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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.
结合遗传算法和支持向量机的硝基苯酚重叠伏安分析
本文提出了一种将遗传算法(GA)和最小二乘支持向量机(LS-SVM)技术相结合的GA- lssvm方法,为提高回归质量和增强特征信息提取能力提供了一个强大的模型。本文首次应用该方法对邻硝基酚、间硝基酚和对硝基酚进行了同时差分脉冲伏安法多组分测定。LS-SVM技术通过降低计算复杂度而拓宽了SVM的应用范围,因为它只需要求解一组线性方程,而不需要求解二次规划问题。因此,LS-SVM具有较快速求解线性和非线性多元校准的能力。遗传算法是一种基于自然进化和遗传学以及达尔文优胜劣汰理论的概率优化技术。实验证明,GA-LS-SVM方法在存在严重伏安重叠的情况下也是有效的。
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