| 引用本文: | 商亮亮,刘建昌,谭树彬,王国柱,于淼.基于自适应高效递推规范变量分析的多模过程软传感器建模[J].控制理论与应用,2016,33(3):380~386.[点击复制] |
| SHANG Liang-liang,LIU Jian-chang,TAN Shu-bin,WANG Guo-zhu,YU Miao.Soft sensor modeling for multimode process based on adaptive efficient recursive canonical variate analysis[J].Control Theory and Technology,2016,33(3):380~386.[点击复制] |
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| 基于自适应高效递推规范变量分析的多模过程软传感器建模 |
| Soft sensor modeling for multimode process based on adaptive efficient recursive canonical variate analysis |
| 摘要点击 2532 全文点击 1994 投稿时间：2015-06-29 修订日期：2015-10-23 |
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| DOI编号 10.7641/CTA.2016.50568 |
| 2016,33(3):380-386 |
| 中文关键词 一阶干扰理论 多模过程 规范变量分析 可变遗忘因子 软传感器 |
| 英文关键词 first order perturbation theory multimode process canonical variate analysis variable forgetting factor soft sensor |
| 基金项目 国家自然科学基金(61374137), 流程工业综合自动化国家重点实验室基础科研业务费(2013ZCX02–03)资助. |
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| 中文摘要 |
| 由于多模过程中各模式间的均值和协方差发生了改变, 多变量单模高斯分布的基本假设不再成立. 基于递
推方法的多模过程软传感器建模存在两点问题: 其一, 递推建模方法不能及时的跟踪多模过程的改变; 其二, 递推
建模方法的在线计算负荷非常高. 为了解决上述问题, 本文提出了一种基于自适应高效递推规范变量分析的多模
过程软传感器建模方法. 首先, 采用指数权重滑动平均来更新过去观测矢量的协方差矩阵; 然后, 利用基于模型输
出误差范数的可变遗忘因子来跟踪多模过程的动态变化; 最后, 通过引入一阶干扰理论(first order perturbation,
FOP) 来实现递推奇异值分解, 与常规奇异值分解相比递推奇异值算法的计算负荷显著降低. 将提出的方法用于田
纳西伊斯曼(tennessee eastman, TE)化工过程进行仿真验证, 其结果表明了该方法的可行性和精确性. |
| 英文摘要 |
| Because of the mean shift and covariance changes between process modes, the basic assumption of Gaussian
distribution for the multivariate unimode processes does not hold for the complex multimode processes. Disadvantages of
soft sensor based on recursive modeling approach for multimode processes include the following two points: first, recursive
methods have difficulty in timely tracking with the changes of multimode processes; second, the online computational cost
of recursive modeling becomes much more expensive. To solve these problems, we propose a soft sensor for multimode
processes on the basis of the adaptive efficient recursive canonical variate analysis (AERCVA) modeling approach. First,
the exponential weighted moving average is adopted to update the covariance of past observation vectors. Then, the variable
forgetting factor based on the norm of output residuals is used to track the changes of the multimode process. Finally, the
first order perturbation (FOP) is introduced to realize recursive singular value decomposition (RSVD), the computation cost
of RSVD is significantly reduced in comparison with the conventional singular value decomposition. The proposed method
is validated in the Tennessee Eastman benchmark process. Simulation results demonstrate the feasibility and accuracy of
the proposed method. |
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