A comparison study between integrated OBFARX-NN and OBF-NN for modeling of nonlinear systems in extended regions of operation
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 nonl...
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| Main Authors: | , , , |
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| Format: | Article |
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Trans Tech Publications Ltd
2014
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| Online Access: | 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 http://eprints.utp.edu.my/31971/ |
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| Summary: | 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. |
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