A Comparison Study between Integrated OBFARX-NN and OBF-NN for Modeling of Nonlinear Systems in Extended Regions of Operation Academic Article uri icon

abstract

  • In this paper the combination of linear and nonlinear models in parallel for nonlinear system identification is investigated. A residuals-based sequential identification algorithm using parallel integration of linear Orthornormal basis filters-Auto regressive with exogenous input (OBFARX) and a nonlinear neural network (NN) models is developed. The model performance is then compared against previously developed parallel OBF-NN model in a nonlinear CSTR case study in extended regions of operation (i.e. extrapolation capability).

publication date

  • 2014

number of pages

  • 3

start page

  • 382

end page

  • 385

volume

  • 625