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Identification of nonlinear parameter varying systems with missing output data
Author(s) -
Deng Jing,
Huang Biao
Publication year - 2012
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.13735
Subject(s) - identification (biology) , computation , nonlinear system , particle filter , computer science , likelihood function , missing data , function (biology) , filter (signal processing) , work (physics) , algorithm , system identification , scale (ratio) , mathematical optimization , estimation theory , engineering , mathematics , data mining , machine learning , biology , measure (data warehouse) , mechanical engineering , botany , physics , quantum mechanics , evolutionary biology , computer vision
An identification of nonlinear parameter varying systems using particle filter under the framework of the expectation‐maximizaiton (EM) algorithm is described. In chemical industries, processes are often designed to perform tasks under various operating conditions. To circumvent the modeling difficulties rendered by multiple operating conditions and the transitions between different working points, the EM algorithm, which iteratively increases the likelihood function, is applied. Meanwhile the missing output data problem which is common in real industry is also considered in this work. Particle filters are adopted to deal with the computation of expectation functions. The efficiency of the proposed method is illustrated through simulated examples and a pilot‐scale experiment. © 2012 American Institute of Chemical Engineers AIChE J, 2012