Abstract
It is nowadays well known that neural networks can model very efficiently complex nonlinear systems. This paper solves the identification problem of a robotic manipulator using dynamical neural networks. More explicitly a dynamic, distributed backpropagation network with two hidden layers and a novice algorithm are used. The network includes dynamic el-ements in its neurons, and this property makes it effective in identifying dynamic nonlinear systems. Simulation results demonstrate the applicability of the approach.
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© 1991 Springer Science+Business Media Dordrecht
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Kosmatopoulos, E.B., Chassiakos, A., Christodoulou, M.A. (1991). Robot Identification using Dynamical Neural Networks. In: Tzafestas, S.G. (eds) Engineering Systems with Intelligence. Microprocessor-Based and Intelligent Systems Engineering, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-2560-4_22
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DOI: https://doi.org/10.1007/978-94-011-2560-4_22
Publisher Name: Springer, Dordrecht
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