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Identification and Predictive Control Using RecurrentNeural Networks
Örebro University, School of Science and Technology, Örebro University, Sweden.
2012 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this thesis, a special class of Recurrent Neural Networks (RNN) is employed

for system identification and predictive control of time dependent systems.

Fundamental architectures and learning algorithms of RNNs are studied

upon which a generalized architecture over a class of state-space represented

networks is proposed and formulated. Levenberg-Marquardt (LM) learning

algorithm is derived for this architecture and a number of enhancements are

introduced. Furthermore, using this recurrent neural network as a system identifier,

a Model Predictive Controller (MPC) is established which solves the optimization

problem using an iterative approach based on the LM algorithm.

Simulation results show that the new architecture accompanied by LM learning

algorithm outperforms some of existing methods. The third approach which

utilizes the proposed method in on-line system identification enhances the identification/

control process even more.

Place, publisher, year, edition, pages
2012. , 103 p.
National Category
Computer Engineering
URN: urn:nbn:se:oru:diva-21762ISRN: ORU-NAT/DAT-AS-2012/0003--SEOAI: diva2:505101
Subject / course
Computer Engineering
Available from: 2012-02-23 Created: 2012-02-23

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School of Science and Technology, Örebro University, Sweden
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