Improving the Identification Performance of an Industrial Process Using Multiple Neural Networks

Modelling or identification of industrial plants is the first and most crucial step in their implementation process. Artificial neural networks (ANNs) as a powerful tool for modelling have been offered in recent years. Industrial processes 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 this method are not accurate enough or the dynamic 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 multiple neural networks (MNNs) for modelling. Bagging and boosting are two methods 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. Simulation results show that the use of MNNs improves the model performance.most popular ones include some elaboration of bagging[6-9], and boosting[10-18]. In this work, we parallel the use of bagging and boosting methods in modelling a chemical plant (CSTR) and compare the results against the corresponding SNN model.