基于Kernel特征空间分解的组分仪递推模型

提出一种Kernel映射空间中特征值问题的递推求解算法，用于建立能够在线快速更新的软组分仪模型。该算法由向前更新和向后更新两个递推阶段组成，只需极小的计算量即可获得新的特征空间信息，且无需保存整个Kernel矩阵。通过对Tennessee Eastman（TE）过程的终端产品质量的建模研究表明，基于提出的快速更新算法建立的软组分仪模型可以获得准确的预报精度，而且在过程故障情况下也显著优于无在线更新的组分仪模型。

Recursive analyzer modeling using Kernel-based eigenspace decomposition method

A recursive Kernel eigenspace updating algorithm was proposed to build the soft sensor for end-product quality.The updating procedure was composed of two sub-stages,i.e.firstly performing forward increasing updating and then followed by backward decreasing updating,which drastically decreased the required computation workload.Further,the whole Kernel matrix did not need to be stored.Simulation study on the Tennessee Eastman process showed that the consequent impurity component model had satisfying precision under both normal and faulty operations,which was obviously superior to the offline batch model and meanwhile approximated the performance of model obtained by successively applying the time-consuming traditional eigenvalue numerical algorithm.