Abstract
Modelling or identification o f industrial p lants is the first and most crucial step in their imp lementation proc- ess. Artificial neural networks (ANNs) as a powerful tool for modelling have been offered in recent years. Industrial proc- esses are often so complicated that using a single neural network (SNN) is not optimal. SNNs in dealing with complex processes do not perform as required. For example the process models with th is method are not accurate enough or the dy- namic characteristics of the system are not adequately represented. SNNs are generally non-robust and they are sometimes over fitted. So in this paper, we use multip le neural networks (MNNs) for modelling. Bagging and boosting are two meth- ods employed to construct MNNs. Here, we concentrate on the use of these two methods in modelling a continuous stirred tank reactor (CSTR) and compare the results against the SNN model. Simu lation results show that the use of MNNs im- proves the model performance.
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