文档介绍：Robust and Efficient Implementation of Strategies for Chemical Engineering Regression Problems
Víctor H. Alvarez, Raquel M. Maduro, Martin Aznar
School of Chemical Engineering, State University of Campinas, UNICAMP, Campinas, . Box 6066, 13083-970, Campinas-SP, Brazil
Abstract
In this work, the use of classical least-squares error for parameter estimation when applied to chemical engineering problems is questioned. Considerable potential now exists for improvements in data fit with the introduction of robust statistical estimators. The τ-estimator, which is an estimator with high breakdown point and high efficiency, was implemented using a ic algorithm (GA) and applied to model fit in chemical engineering problems. The method was validated and applied to three chemical engineering problems, parison between least-squares and τ-estimator. The results show that the τ-estimator coupled with GA produces a best data fit.
Keywords: non-linear regression, high breakdown point, ic algorithm.
Introduction
The most widely used model formalization is the assumption that the experimental data have a normal (Gaussian) distribution of the errors. The question arises as to whether the assumptions and methods that were taught are still sufficient for the data analysis needs of chemical engineer. For example, Clancey [1] examined approximately 250 error distributions involving 50000 c