【作者】 陈渭泉；

【导师】 苏宏业；

【作者基本信息】 浙江大学 ， 控制理论与控制工程， 2004， 硕士

【摘要】 软测量技术近年来已经得到了广泛的重视和应用，在过程检测和控制系统的发展中发挥着越来越大的作用。软测量技术的基本原理是根据某种最优准则，选择一组在工业上容易检测而且与主导变量有密切关系的辅助变量，通过构造某种数学关系，用计算机软件实现对主导变量的在线估计。从上述定义可以看出，辅助变量的选择对建立一个成熟有效的软测量模型至关重要。所谓辅助变量的选择就是在一系列预先给定的自变量集合中找出其中的一个子集，使得这个子集能够对因变量进行最好的描述，或者找出一个变量集的子集，使得这个子集能够包含较少的变量，同时能够尽可能地保持原来的完整数据集的多元结构特征。通过辅助变量的选择，不仅可以使软测量模型得到简化，使模型更加容易理解，而且从经济上能大大降低信息收集的成本。本论文针对辅助变量的选择进行了研究。使用的主要分析工具是主元分析(PCA：Principle Component Analysis)方法和偏最小二乘(PLS：Partial Least Square)方法，将这些方法结合遗传算法和后向回归等搜索算法来寻找“最优”自变量子集。本论文的主要研究工作概括如下： (1) 提出了一种基于PLS和PCA相结合的变量选择方法； (2) 提出了一种在PLS回归中选择PLS主成分个数的方法； (3) 提出了一种用遗传算法和贝叶斯统计方法相结合的变量选择方法，并通过仿真例子验证了其有效性； (4) 利用实际的工业数据，运用变量选择方法建立了一个关于4-CBA含量的软测量模型。 最后，在总结全文的基础上，探讨了变量选择技术有待进一步研究和探索的问题。更多还原

【Abstract】 Soft sensor technique has already been widely used and plays a more and more important role in the development of process detection and control system in recent years. Its basic principle is to select a set of secondary variables that are easy to detect and have close relationship with the primary variable according to certain "optimal" criteria. The selected secondary variables are then used to obtain the on-line estimation of the primary variable by constructing some mathematic relationship between these variables. It can be seen from the above definition that the selection of secondary variables is of great importance for building an effective and mature soft sensor model and this dissertation devoted to this problem. The selection of secondary variables is to find a subset of a pre-specified set of independent variables, which can best describe the dependent variables, or to select a subset of the original data which contains a relatively a small number of variables in such a way that the selected subsets of variables retain, as much as possible, the overall multivariate structure of the complete original data. Through variable selection, we can not only simplify the soft sensor model and make it easier to understand, but also greatly reduce the cost of information collection. This dissertation uses principal component analysis and partial least square as major mathematic tools. We use backward regression and genetic algorithm to find the "optimal subset". The main research work conducted in this dissertation summarized as follows:1. A variable selection method is proposed by using PC A and PLS;2. A new method is proposed to select the number of principal component of PLS in PLS regression;3. A variable selection method is proposed based on the combination of genetic algorithm and Bayesian statistics and verified by simulation data;4. A soft sensor model on the concentration of 4-CBA is proposed by using variable selection method based on real industrial data.Finally, several problems for further research and exploration are proposed based on the summary of the research results.更多还原