MODELLING OF THE DYNAMICS OF A GYROSCOPE USING ARTIFICIAL NEURAL NETWORKS

MODELLING OF THE DYNAMICS OF A GYROSCOPE USING ARTIFICIAL NEURAL NETWORKS

It this paper, a neural network was utilized in order to create an emulator, which could mimic the behaviour and nonlinear dynamics of a gyroscope with two axes of freedom, subjected to both low- and high-frequency excitation. For this purpose, several known learning methods, such as the gradient an...

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Veröffentlicht in:Journal of Theoretical and Applied Mechanics (Warsaw) 2012-01, Vol.50 (1), p.85-97
1. Verfasser: Lacny, Lukasz
Format: Artikel
Sprache:eng
Schlagworte:
Artificial neural networks
Emulators
Gyroscopes
Learning
Networks
Neural networks
Nonlinear dynamics
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Beschreibung
Zusammenfassung:It this paper, a neural network was utilized in order to create an emulator, which could mimic the behaviour and nonlinear dynamics of a gyroscope with two axes of freedom, subjected to both low- and high-frequency excitation. For this purpose, several known learning methods, such as the gradient and Levenberg-Margquardt method, were used. Three different models of neural networks were considered and compared for their effectiveness: NNFIR, NNARX and the recurrent network NNARMAX.
ISSN:1429-2955