【作者】 杜万亮；

【导师】 张颖伟；

【作者基本信息】 东北大学 ， 控制理论与控制工程， 2009， 硕士

【摘要】 本文以多变量回归模型问题为主要内容,利用现代信号处理技术,针对现有基于数据驱动的多变量统计过程预测方法的不足,引入独立元分析(Independent Component Analysis,简称ICA)应对实际工业过程中非高斯问题。本文在分析ICA提取的独立元的性质的基础上,提出了基于修正独立元分析的多变量回归方法。回归分析中提取的特征成分包含目标信息的丰富程度对模型的精度影响很大,为了提取包含目标信息丰富信息的特征成分,本文首先利用修正的ICA方法提取独立元建立类似主元的回归模型,接着对建立的模型进行改进：对输入变量进行用于回归的特征独立元提取,使得提取的输入变量的独立元和目标变量的独立元互信息最大,从而建立回归模型。接着为了解决非线性问题,介绍了在输出空间利用核独立元分析提取非线性的独立元用于回归建模。本文的基于ICA的回归方法对田纳西—库兹曼过程的部分过程测量变量和成分测量变量进行了预测研究,通过一些过程测量变量把本文的基于ICA的回归方法和偏最小二乘回归方法进行了比较,基于TE过程的仿真实验证实了其有效性。接着对部分成分测量变量进行了预测研究,证实核独立元提取的特征对非线性回归的有效性。更多还原

【Abstract】 With the help of modern signal processing techniques, based on existing data-driven multivariate statistical forecasting methods of the process predictions methods. This paper studies process prediction methods within the application of data-driven multivariate statisti-cal process predictions methods in the flow industry. Flow industry is an essential part of our national economy. As the process involves high temperature, high pressure and high risk. the importance of its quality prediction is increasingly prominent.Based on the analysis of ICs extracted from the nature of an independent element on the basis of the proposed amendments based on independent component analysis of multi-variable regression method. Regression analysis of the characteristics of components to extract target information included in the model of the abundance of great influence on the accuracy, In this paper, the first to use ICA to extract the amendment to establish a similar independent component of the PCA regression model, Followed by the establishment of the model to improve:The input variables for the characteristics of independent reunification Extraction, Makes extraction of the input variables and objectives of Independent Independent variables largest mutual information, regression model to build. In order to solve nonlinear problems and then introduced in the output space using the nuclear extraction independent component analysis for non-linear regression prediction.The method is applied to the quality prediction of Tennessee—Eastman Process in this paper. The prediction performance of the proposed approach based ICA is compared to PLSR using some process variables examples. It is proved to be effective through the simulate application upon TE process. And use KICA to extract features of target variables to predict the quality of some process. The simulation result shows that the ICA method can capture the nonlinear dynamic features effectively, and predict the process quality successfully.更多还原