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.

Highlights

  • In recent decades, artificial neural networks (ANNs) have been extensively used in numerous applications

  • Thevalues tabulated for the square of the correlation coefficients(R-Squared) indicate that the regression in the multip le neural networks (MNNs) is better than the single neural network (SNN)

  • The second column of the table shows that using the boosting algorithm leads to reductions of both of the error variance and modeling error in the MNN when co mpared against the SNN

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Summary

Introduction

Artificial neural networks (ANNs) have been extensively used in numerous applications. One of the important applications of ANNs is finding patterns or tendencies in data. One problem with ANNs is their instability. It means that small changes in the training data used to construct the model may result in a very dissimilar model[4]. Due to the high variance of SNNs, the model may exh ibit quite a different accuracy facing unseen data (validation stage)[4]. In nu merous cases, a SNN lacks precision. Breiman[5] has shown that for unstable predictors, combining the outputs of a number of models will reduce variance and give more precise predictio n s

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