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• W2085669162 abstract "Chemical industries are characterized often by nonlinear processes. Therefore, it is often difficult to obtain nonlinear models that accurately describe a plant in all regimes. The main contribution of this work is to establish a reliable model of a process behavior. The use of this model should reflect the normal behavior of the process and allow distinguishing it from an abnormal one. Consequently, the black-box identification based on the neural network (NN) approach by means of a nonlinear autoregressive with exogenous input (NARX) model has been chosen in this study. A comparison with an autoregressive with exogenous input (ARX) model based on the least squares criterion is carried out. This study also shows the choice and the performance of ARX and NARX models in the training and test phases. Statistical criteria are used for the validation of the experimental data of these approaches. The identified neural model is implemented by training a multilayer perceptron artificial neural network (MLP-ANN) with input–output experimental data. An analysis of the inputs number, hidden neurons and their influence on the behavior of the neural predictor is carried out. In order to illustrate the proposed ideas, a reactor-exchanger is used. Satisfactory agreement between identified and experimental data is found and results show that the neural model predicts the evolution of the process dynamics in a better way. Copyright © 2008 Curtin University of Technology and John Wiley & Sons, Ltd." @default.
• W2085669162 created "2016-06-24" @default.
• W2085669162 creator A5067123750 @default.
• W2085669162 date "2008-11-01" @default.
• W2085669162 modified "2023-10-18" @default.
• W2085669162 title "Using ARX and NARX approaches for modeling and prediction of the process behavior: application to a reactor-exchanger" @default.
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• W2085669162 doi "https://doi.org/10.1002/apj.118" @default.
• W2085669162 hasPublicationYear "2008" @default.
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