支持向量机在工业过程中的应用

支持向量机(SVM)是一种基于统计学习理论，针对小样本学习问题的通用学习算法，它采用结构风险最小化(Structural risk minimization，SRM)准则，大大提高了模型的泛化能力，成功地解决了神经网络的过学习问题。目前主要应用在模式识别领域，在工业过程中的应用相对较少。本文首先从理论研究、算法结构、参数选择和扩展SVM4个方面详细介绍了近些年来支持向量机的研究进展；然后对SVM在工业过程中的应用现状进行分析，指出进一步研究的方向。

Application of support vector machine in industrial process

Support vector machine(SVM) is new learning machine based on statistical learning theory, which is a kind of learning algorithm focused on small sample. It has improved the ability of generation greatly and solved the over-fitting problem of neural network successfully by using the principle of structural risk minimization. Recently SVM has been used in the field of pattern recognition, however much less in the field of industrial process. This article firstly introduces the development of SVM from the following sides : theory research, algorithm configuration, parameter selection and expanding SVM, then analyzes the applications of SVM to industrial process, such as fault diagnosis, process modeling, system identification, and nonlinear control and so on. Finally, the future research directions are pointed out.