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MLP and Elman recurrent neural network modelling for the TRMS

Toha, Siti Fauziah and Tokhi, M. Osman (2008) MLP and Elman recurrent neural network modelling for the TRMS. In: 7th IEEE International Conference on Cybernetic Intelligent Systems (CIS08), 9-10 September 2008, London, U.K..

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Abstract

This paper presents a scrutinized investigation on system identification using artificial neural network (ANNs). The main goal for this work is to emphasis the potential benefits of this architecture for real system identification. Among the most prevalent networks are multi-layered perceptron NNs using Levenberg-Marquardt (LM) training algorithm and Elman recurrent NNs. These methods are used for the identification of a twin rotor multi-input multi-output system (TRMS). The TRMS can be perceived as a static test rig for an air vehicle with formidable control challenges. Therefore, an analysis in modeling of nonlinear aerodynamic function is needed and carried out in both time and frequency domains based on observed input and output data. Experimental results are obtained using a laboratory set-up system, confirming the viability and effectiveness of the proposed methodology.

Item Type: Conference or Workshop Item (Full Paper)
Additional Information: 4680/7128
Uncontrolled Keywords: Multi layer perceptron neural network (MLP-NN), Levenberg-Marquardt, twin rotor MIMO system (TRMS), Elman neural network
Subjects: T Technology > T Technology (General) > T173.2 Technological change
Kulliyyahs/Centres/Divisions/Institutes: Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User: Dr. Siti Fauziah Toha
Date Deposited: 11 Jun 2012 08:34
Last Modified: 02 Oct 2012 08:07
URI: http://irep.iium.edu.my/id/eprint/7128

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