探究支持向量机算法在化学化工中的应用

计算机技术的快速发展,给化学化工数据的处理带来极大便利。通过机器学习算法,可以总结化学化工实验规律,控制化工生产过程。原有的机器算法虽能为化学化工带来很大便利,但是它本身就存在缺陷。机器学习算法的核心是数学中的渐近理论,这项理论的适用情景是必须有大量的样本,而实际的化学化工工作中样本有限,这就可能导致计算中的过拟合。为了解决这一弊病,我们采用了向量机算法取代原有的机器算法,目前使用支持向量机算法(SVM)建立数学模型已经得到国内外的广泛关注。笔者通过调查化学化工行业中SVM的使用情况,阐述了向量机算法的优势,分析了它在食品检验、化工生产等多项领域的应用。

Application of the Support Vector Machine Algorithm in Chemistry and Chemical Engineering

The rapid development of computer technology brings great convenience for processing of chemistry and chemical data. By machine learning algorithms, chemistry experiment law can be summarized to control chemical production process. Although the original machine algorithm can bring great convenience for the chemical industry, but it has defects itself. The core of machine learning algorithms is mathematical asymptotic theory, this theory is applicable to the scenario that must have a large number of samples, but the actual work samples in chemistry and chemical engineering are limited, which can lead to the over-fitting of calculation. To overcome this drawback, the support vector machine algorithm （SVM） has been used to replace the original machine algorithms; currently the support vector machine algorithm （SVM） has been widely used to establish mathematical models at home and abroad. In this paper, application of SVM in the chemical industry was introduced, advantages of SVM were described, and its anolication prosoect in various fields was analvzed.