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Nonlinear system identification using integrated linear-NN models: series vs. parallel structures

H., Zabiri and M., Ramasamy and Lemma D, Tufa and Maulud, Abdulhalim (2011) Nonlinear system identification using integrated linear-NN models: series vs. parallel structures. In: 2011 International Conference on Modeling, Simulation and Control (IPCSIT), 2-4 September 2013, Singapore.

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Official URL: http://www.ipcsit.com/vol10/7-ICMSC2011S021.pdf

Abstract

In this paper, the performance of integrated linear-NN models is investigated for nonlinear system identification using two different structures: series vs. parallel. In particular, Laguerre filters are selected as the linear models, and multi-layer perceptron (MLP) or feed-forward neural networks (NN) are selected for the nonlinear models. Results show promising capability of the (novel) parallel Laguerre-NN structure especially in terms of its generalization capability when subjected to data different from those used during the identification stage in comparison to the series Laguerre-NN.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:nonlinear system identification, parallel integration, OBF, MLP, extrapolation
Subjects:T Technology > TP Chemical technology
Academic Subject One:Academic Department - Chemical Engineering - Advance Process Control
Departments / MOR / COE:Departments > Chemical Engineering
ID Code:10747
Deposited By: Haslinda Zabiri
Deposited On:16 Dec 2013 23:48
Last Modified:16 Dec 2013 23:48

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