A Novel Robust Nonlinear Dynamic Data Reconciliation

在一个变量的孤立点将在数据和解(医生) 涂另外的大小的评价。在这篇文章，一个新奇柔韧的方法为非线性的动态数据和解被建议，到在这个方法介绍的医生的结果上减少孤立点的影响在常规最少平方的客观功能的一个惩罚功能矩阵，为正常大小为孤立点和大重量分配小重量。避免数据信息的损失，元素明智的 Mahalanobis 距离被建议作为向量明智的距离上的改进，工作构造惩罚矩阵。测量错误的关联也在这篇文章被考虑。方法由构造惩罚重量矩阵介绍柔韧的统计理论进常规最不方形的评估者并且得到不仅好坚韧性而且简单计算。一个连续搅动的坦克反应堆的模拟，验证建议算法的有效性。

Outlier in one variable will smear the estimation of other measurements in data reconciliation （DR）. In this article, a novel robust method is proposed for nonlinear dynamic data reconciliation, to reduce the influence of outliers on the result of DR. This method introduces a penalty function matrix in a conventional least-square objective function, to assign small weights for outliers and large weights for normal measurements. To avoid the loss of data information, element-wise Mahalanobis distance is proposed, as an improvement on vector-wise distance, to construct a penalty function matrix. The correlation of measurement error is also considered in this article. The method introduces the robust statistical theory into conventional least square estimator by constructing the penalty weight matrix and gets not only good robustness but also simple calculation. Simulation of a continuous stirred tank reactor, verifies the effectiveness of the proposed algorithm.