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L. Ljung, “Perspectives on system identification,” Annual Reviews in Control, vol. 34, pp. 1-12, 2010.

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Article

Robust Identification of Hydrocarbon Debutanizer Unit using Radial Basis Function Neural Networks (RBFNNs)

1Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran

2Department of Basic Science, Petroleum University of Technology, Ahwaz, Iran

3Senior expert in R&D, Sarkhon & Qeshm Gas Company, Bandar Abbas, Iran


Journal of Automation and Control. 2015, Vol. 3 No. 1, 10-17
DOI: 10.12691/automation-3-1-2
Copyright © 2015 Science and Education Publishing

Cite this paper:
Masih Vafaee Ayouri, Mehdi Shahbazian, Bahman Moslemi, Mahboobeh Taheri. Robust Identification of Hydrocarbon Debutanizer Unit using Radial Basis Function Neural Networks (RBFNNs). Journal of Automation and Control. 2015; 3(1):10-17. doi: 10.12691/automation-3-1-2.

Correspondence to: Masih  Vafaee Ayouri, Department of Instrumentation and Automation Engineering, Petroleum University of Technology, Ahwaz, Iran. Email: masih.vafaee@gmail.com

Abstract

Radial Basis Function Neural Network (RBFNN) is considered as a good applicant for the prediction problems due to it’s fast convergence speed and rapid capacity of learning, therefore, has been applied successfully to nonlinear system identification. The traditional RBF networks have two primary problems. The first one is that the network performance is very likely to be affected by noise and outliers. The second problem is about the determination of the parameters of hidden nodes. In this paper, a novel method for robust nonlinear system identification is constructed to overcome the problems of traditional RBFNNs. This method based on using Support Vector Regression (SVR) approach as a robust procedure for determining the initial structure of RBF Neural Network. Using Genetic Algorithm (GA) for training SVR and select the best parameters as an initialization of RBFNNs. In the training stage an Annealing Robust Learning Algorithm (ARLA) has been used for make the networks robust against noise and outliers. The next step is the implementation of the proposed method on the Hydrocarbon Debutanizer unit for prediction of n-butane (C4) content. The performance of the proposed method (ARLA-RBFNNs) has been compared with the conventional RBF Neural Network approach. The simulation results show the superiority of ARLA-RBFNNs for process identification with uncertainty.

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