%0 Journal Article
%@ 16609336
%A Zabiri, H.
%A Ariff, M.
%A Tufa, L.D.
%A Ramasamy, M.
%D 2014
%F scholars:4777
%I Trans Tech Publications Ltd
%J Applied Mechanics and Materials
%K Algorithms; Models, Comparison study; Identification algorithms; Linear and nonlinear models; Model performance; NN; Nonlinear neural networks; OBFARX; Parallel integration, Nonlinear systems
%P 382-385
%R 10.4028/www.scientific.net/AMM.625.382
%T A comparison study between integrated OBFARX-NN and OBF-NN for modeling of nonlinear systems in extended regions of operation
%U https://khub.utp.edu.my/scholars/4777/
%V 625
%X 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). © 2014 Trans Tech Publications, Switzerland.
%Z cited By 0; Conference of 3rd International Conference on Process Engineering and Advanced Materials, ICPEAM 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:114811