Abstract
It is nowadays well known that neural networks can model nonlinear dynamical systems. This paper solves an identification problem of a robot manipulator which is moving on a constraint surface, by using dynamical neural networks. More explicitly we use Differential/Algebraic Recurrent High Order Neural Networks (D/A-RHONNs) with a learning algorithm which is based on Lyapunov stability theory. The network consists of a combination of differential algebraic equations, and this property makes it effective in identifying nonlinear differential/algebraic systems. Simulation results demonstrate the applicability of the approach.
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© 1995 Springer Science+Business Media Dordrecht
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Kosmatopoulos, E.B., Christodoulou, M.A. (1995). Identification of Constrained Robot Dynamics Using Dynamic Neural Networks. In: Janssen, J., Skiadas, C.H., Zopounidis, C. (eds) Advances in Stochastic Modelling and Data Analysis. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0663-6_23
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DOI: https://doi.org/10.1007/978-94-017-0663-6_23
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