文献类型：期刊文献

英文题名：Pre-analysis of multi-batch bioprocesses data with finite mixture models in the reduced feature subspace

作者：Lin, Weilu[1]; Martin, Elaine[2]; Montague, Gary[2]

机构：[1] State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China; [2] School of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom

年份：2010

卷号：9

期号：PART 1

起止页码：19

外文期刊名：IFAC Proceedings Volumes (IFAC-PapersOnline)

收录：EI(收录号：20113414248386)

语种：英文

外文关键词：Maximum principle - Fisher information matrix - Discriminant analysis - Gaussian distribution

摘要：Multi-batch bioprocesses data, unlike the data from other industries, are highly correlated due to the operation characteristics of the industry. In this work, pairwise Fisher discriminant analysis (FDA) is successfully utilized to reveal the similarity between two batches. In order to handle the mixture pattern for the data projected into the reduced feature subspace represented by the first several generalized eigenvectors, the finite Gaussian mixture model is adopted here to calculate the confidence region of each mixture. There are several challenges facing application engineers when estimate finite mixture models (FMMs), such as initialization of the expectation-maximization (EM) algorithm and determination of number of mixtures. In this work, an initialization method based on the uniform prior distribution assumption and a new method to determine the number of components of FMMs based on estimated density histogram are proposed. The utility of the proposed method has been demonstrated in simulation studies. Combined with the pairwise FDA, the method has been successfully applied to a large scale multi-batch bioprocess data set. ? 2009 IFAC.