【作者】 苏盈盈；

【导师】 肖蕙蕙；

【作者基本信息】 重庆工学院 ， 测试计量技术及仪器， 2008， 硕士

【摘要】 目前使用的软传感器模型辅助变量较多,各变量之间往往存在多重相关性,致使模型的预测精度不高,泛化性能和稳定性较差。针对此问题,本文在系统探讨各种特征提取方法的基础上,利用特征子空间思想去除这种相关性,形成基于特征子空间回归的软传感器精简化建模方法,主要包括PCA+NN,PCA+SVR,KPCA+NN,KPCA+SVR,KPLS(KCCA+LS)。同时,该类方法可解决先验知识很少的对象的建模问题。该方法首先针对辅助变量间的线性/非线性相关问题,分别利用各种特征提取方法去除冗余信息,提高样本数据的质量。主要包括线性主成分分析PCA(Principal Component Analysis)、核主成分分析KPCA(Kernel PCA)、核典型相关分析KCCA(Kernel Canonical Correlation Analysis)方法。然后,在信息充分且不相关的样本数据的基础上,可利用信息融合方法对软传感器对象进行建模和预测。主要包括基于结构风险最小化的支持向量回归机SVR(Support Vector Regression)和基于经验风险最小化的神经网络NN(Neural Network)、最小二乘LS(Least Squares)方法。最后,用AIC信息准则(Akaike Information Criterion)为评价函数,综合评价出具有较高模型精度、较好泛化能力和较低复杂度的最佳模型。为了验证该类方法的可行性及有效性,分别针对线性动态软传感器模型、非线性静态软传感器模型、非线性动态软传感器模型,进行了仿真对比研究。结果表明:①针对相关的样本数据,采用特征提取结合信息融合的建模方法比单独使用信息融合更为有效;②PCA+SVR在解决线性软传感器建模时精度和泛化能力较佳;③KPCA+SVR在解决非线性静态/动态软传感器建模时具有较佳的精度和泛化能力。最后,本文针对一种新型的睡眠躁动软传感器,利用上述五种精简化建模方法进行了实例对比研究。结果表明:在综合考虑软传感器模型复杂度、精度和泛化能力的情况下,利用AIC准则评价的PCA+NN方法在解决该问题时具有最佳的效果。更多还原

【Abstract】 Nowadays, there are many correlated secondary variables of soft sensor for complex objects, which make poor performance, low generalization and bad stability. Aiming at the problem, after the discussion of some feature extraction and regression methodology several parsimoniously modeling methodology are presented based on Feature Subspace Regression, such as PCA+NN, PCA+SVR, KPCA+NN, KPCA+SVR, KPLS (KCCA+LS), etc. Meanwhile, the methodology can solve the modeling problem of soft sensor with seldom prior knowledge.It firstly uses feature extraction method, as PCA (Principal Component Analysis) CCA (Canonical Correlation Analysis) or KPCA (Kernel PCA) or KCCA (Kernel CCA) to extract the input matrix of second variable, obtaining the linear/nonlinear uncorrelated sample data. On the basis, the SVR (Support Vector Regression) with SRM (Structure Risk Minimization) or NN (Neural Network) or LS (Least Squares) with ERM (Empirical Risk Minimization) are utilized to realize the structure identification of information fusion part. In this way, performance, stability and generalization of soft sensor are improved.Furthermore, the accuracy and the complexity of the modeled plant are estimated by AIC (Akaike Information Criterion). The smaller AIC value is, the better modeling structure is. In order to verify the effectivity, three types of soft sensor with linear dynamic plant, nonlinear static plant and nonlinear dynamic plant are studied with different parsimonious methods. Simulation results show that KPLS is good for its high accuracy, PCA+SVR method is most effective when solving soft sensor modeling with linear plant, and to nonlinear plant soft sensing problem, KPCA+SVR method is more excellent.On the basis, a new type of sleeping fidget soft sensor is presented and also studied as the modeling problem. Compared with some parsimonious modeling methods, PCA+NN is evaluated best with AIC criterion of high accuracy, good generalization and low complexity to this problem.更多还原