@article{scholars4777,
          volume = {625},
         journal = {Applied Mechanics and Materials},
           pages = {382--385},
       publisher = {Trans Tech Publications Ltd},
            note = {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},
           title = {A comparison study between integrated OBFARX-NN and OBF-NN for modeling of nonlinear systems in extended regions of operation},
            year = {2014},
             doi = {10.4028/www.scientific.net/AMM.625.382},
        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). {\^A}{\copyright} 2014 Trans Tech Publications, Switzerland.},
            issn = {16609336},
            isbn = {9783038351818},
          author = {Zabiri, H. and Ariff, M. and Tufa, L. D. and Ramasamy, M.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84914129329&doi=10.4028\%2fwww.scientific.net\%2fAMM.625.382&partnerID=40&md5=ba98477ccebbba4d5aba420f3b7c91c2},
        keywords = {Algorithms; Models, Comparison study; Identification algorithms; Linear and nonlinear models; Model performance; NN; Nonlinear neural networks; OBFARX; Parallel integration, Nonlinear systems}
}