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Digital Library

of the European Council for Modelling and Simulation

 

Title:

Elman Neural Networks In Model Predictive Control

Authors:

David Samek

Published in:

 

(2009).ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera. European Council for Modeling and Simulation. doi:10.7148/2009 

 

ISBN: 978-0-9553018-8-9

 

23rd European Conference on Modelling and Simulation,

Madrid, June 9-12, 2009

Citation format:

Samek, D. (2009). Elman Neural Networks In Model Predictive Control. ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera (pp. 577-581). European Council for Modeling and Simulation. doi:10.7148/2009-0577-0581

DOI:

http://dx.doi.org/10.7148/2009-0577-0581

Abstract:

The goal of this paper is to present interesting way how to model and predict nonlinear systems using recurrent neural network. This type of artificial neural networks is underestimated and marginalized. Nevertheless, it offers superior modelling features at reasonable computational costs. This contribution is focused on Elman Neural Network, two-layered recurrent neural network. The abilities of this network are presented in the nonlinear system control. The task of the controller is to control the liquid level in the second of two interconnected cylindrical tanks. The mathematical model of the real- time system was derived in order to test predictor and consequently the controller in Matlab/Simulink simulations.

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