Abstract:

Soft sensing technique is an effective method to estimate variables which are difficult to be measured on-line in industrial processes, and the core problem of soft sensing technique is construction of an appropriate mathematical model. Support vector machine (SVM) algorithm is a machine learning method based on statistical theory. Least squares support vector machine (LSSVM) is a development of the SVM, and has a faster velocity than the standard SVM. Similar to SVM, LSSVM also has the problem of parameter selection. The differential evolution (DE) method was proposed to select hyper-parameter of LSSVM. At last DE-LSSVM was presented for soft sensor modeling on testing the content of 4-carboxybenzaldehyde (4-CBA) in terephthalic acid, and the result was satisfied.

摘要：

软测量技术是解决工业过程中存在的一类难以在线测量参数估计问题的有效方法，该技术的核心是建立优良的数学模型。支持向量机是基于统计学理论的一种机器学习方法，最小二乘支持向量机是一种扩展的支持向量机，相对于支持向量机具有较快求解速度。最小二乘支持向量机存在着参数选择的问题，针对这个问题，采用差分进化算法进行参数选择。提出基于差分进化算法的最小二乘支持向量机应用于软测量建模，并将其应用于对苯二甲酸中对羧基苯甲醛含量测试的软测量建模中，获得了满意的结果。