%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