Iterative Algorithms for Data Reconciliation Estimator Using Generalized t-Distribution Noise Model

Abstract

The generalized t-distribution (GT) is well-known because of its flexibility in transforming into many popular distributions. However, implementation of data reconciliation (DR) estimator using GT noise is somehow difficult due to its complex structure. This work proposes two iterative algorithms to ease the complexity of the GT DR estimator, hence making it easy to implement even in a large-scale problem. We also point out the convergence condition for each algorithm. Some simulation examples are shown to verify the effectiveness of the proposed algorithms on computational time. The results from this work can also be applied to other data reconciliation estimators.

Programming Strategies of Sequential Incremental-Scale Subproblems for Large Scale Data Reconciliation and Parameter Estimation with Multi-Operational Conditions

“…If the initial value of the optimization variables is far away from the optimum, the large-scale NLP methods usually cannot give convergent results or may converge to a (sub) local optimum for the large scale DRPE problem. Many computational strategies were proposed, such as the reduced successive quadratic programming strategy, the nested three-stage computation framework, particle swarm optimization, parallel calculation methods, , iterative algorithms, and pervasive knowledge discovery strategies by just-in-time learning, etc. When the production of multigrade chemical and polymer products is changed to meet the market demand, it requires the change of the operating conditions as well.…”