Research into New Strategies for Rectification of Uncertain Data in Process Design

在任何设计中，由于偏差、随机误差、大方差、缺失数据和其他无法控制的因素，设计所依据的信息都是不确定的。在工艺设计中，这些不确定性往往被忽略或确定性地处理。人们提出了各种估算和校正数据的策略。这些策略包括统计技术，如最小二乘和最大似然，优化技术，如线性规划和拉格朗日乘法器，以及过滤技术。所有这些技术都侧重于数据本身，而不是在设计过程中使用数据。利用影响法和区间分析，提出了一种新的数据协调方法，以消除数据中的总体误差和系统误差，并对剩余数据进行协调。这种新方法将提高可供设计人员使用的数据的质量。这项研究将产生可应用于许多学科的算法和计算机程序。

In any design, the information upon which the design is based is uncertain due to biases, random errors, large variances, missing data, and other factors that cannot be controlled. In process design, these uncertainties are often ignored or treated deterministically. Various strategies have been proposed for estimating and rectifying the data. Among these strategies are statistical techniques such as least squares and maximum likelihood, optimization techniques such as linear programming and Lagrange multipliers, and filtering techniques. All of these techniques focus on the data itself rather than on the use of the data within the design process. A new approach to data reconciliation is developed using influence methods and interval analysis to eliminate gross and systematic errors in the data and to reconcile the remaining data. This new methodology will enhance the quality of the data available to the designer. The research will result in algorithms and computer programs that will be applicable in a number of disciplines.