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Parameter set selection for estimation of nonlinear dynamic systems
Author(s) -
Chu Yunfei,
Hahn Juergen
Publication year - 2007
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.11295
Subject(s) - parameter space , estimation theory , sensitivity (control systems) , nonlinear system , selection (genetic algorithm) , set (abstract data type) , mathematics , mathematical optimization , sampling (signal processing) , control theory (sociology) , statistics , computer science , engineering , artificial intelligence , physics , control (management) , filter (signal processing) , quantum mechanics , electronic engineering , computer vision , programming language
A new approach is introduced for parameter set selection for nonlinear systems that takes nonlinearity of the parameter‐output sensitivity, the effect that uncertainties in the nominal values of the parameters have and the effect that inputs and initial conditions have on parameter selection into account. In a first step, a collection of (sub)optimal parameter sets is determined for the nominal values of the parameters using a genetic algorithm. These parameter sets are then further analyzed for uncertainty in the parameters and changes in the initial conditions and inputs using differential analysis as well as a sampling‐based approach to determine the key factors influencing sensitivity and the likelihood of a parameter set to be the optimal set under these varying conditions. The outcome of this procedure is a collection of parameter sets, which can be used for parameter estimation and additional information about how likely it is that a set is optimal for parameter estimation. Additionally, the size of the region in parameter space in which a certain set of parameters will remain optimal is determined. © 2007 American Institute of Chemical Engineers AIChE J, 2007