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Nonlinear system identification using radial basis functions
Author(s) -
Mokhasi Paritosh,
Rempfer Dietmar
Publication year - 2009
Publication title -
international journal for numerical methods in fluids
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.938
H-Index - 112
eISSN - 1097-0363
pISSN - 0271-2091
DOI - 10.1002/fld.2112
Subject(s) - nonlinear system , basis (linear algebra) , series (stratigraphy) , computer science , mathematics , system identification , flow (mathematics) , dynamical systems theory , construct (python library) , radial basis function , identification (biology) , dynamical system (definition) , mathematical optimization , algorithm , artificial intelligence , data modeling , physics , geometry , geology , quantum mechanics , database , paleontology , botany , artificial neural network , biology , programming language
Focussing on applications to fluid flow phenomena, we are interested in developing dynamical system models that are based on the discrete multivariate time series information only. A method based on radial basis functions and linear multi‐step methods is used to construct continuous nonlinear models that approximate the original dynamical system. Information, such as the structure of the original system, is incorporated into the models through weak constraints. The formulation of the model and its advantages associated with modeling is described. Different examples are presented that highlight the various characteristics of the model and its effectiveness in dealing with various problems encountered in fluid flow problems. Copyright © 2009 John Wiley & Sons, Ltd.