Abstract
Two-link robot arm model is extensively used in literatures for that it is simple enough to simulate conveniently, yet contains all the nonlinear terms arising in general n-link manipulators. And neural networks are reported to be computationally efficient compared with traditional PID control and adaptive control. However, when a neural network is applied, one of the key step is to choose the optimal number of neurons. In this paper, a relative large number of neurons are initially used, which is pruned during the training. The conic sector theory is introduced in the design of this robust neural control system, which aims at providing guaranteed boundedness for both the input-output(I/O) signals and the weights of the neural network.
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© 2005 Springer-Verlag Berlin Heidelberg
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Ni, J., Song, Q. (2005). Pruning Neural Networks for a Two-Link Robot Control System. In: Cabestany, J., Prieto, A., Sandoval, F. (eds) Computational Intelligence and Bioinspired Systems. IWANN 2005. Lecture Notes in Computer Science, vol 3512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11494669_85
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DOI: https://doi.org/10.1007/11494669_85
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26208-4
Online ISBN: 978-3-540-32106-4
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