Abstract
The artificial neural networks (ANNs) application for computing has currently emerged as an important information processing technique. In some way, the ANNs are a parallel processing artichecture in which a large number of processing neurons are interconnected and the knowledge is represented by the connection weights between the neurons. The connection weights are adjusted through a learning process. The knowlegde is distributed over a large number of connection weights so that the operation of these networks degrade peacefully, even in some parts the connection weights are disconnected. But there is a big problem with this kind of structure. This kind of structure can be a good candidate for simple systems, not for large scale-systems and real applications.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Reference
M. Teshnehlab and K. Watanabe, “Self-tuning of computed torque gains by using neural networks with flexible structure, ” IEE Proceedings-D, to be published,1994.
M. Teshnehlab and K. Watanabe, “The high flexibility and learning capability of neural networks with learning bipolar and unipolar sigmoid functions, ” Japan-U.S.A.symposium on flexible automation, to be presented, Kobe, July 1994.
T. Yamada and T. Yabuta, “Neural Network Controller Using Autotuning Method for Nonlinear Functions,” IEEE Trans. on Neural Networks, Vol. 3, No.4, July, pp.595–601, 1992.
H. Gomi and M. Kawato, “Nueral Network Control for a Closed-Loop System Using Feedback-Error-Learning,” Neural Networks, Vol. 6, No. 7, pp.933–946, 1993.
H. Miyamoto, M. Kawato, T. Setoyama, and R. Suzuki, “Feedback-Error-Learning Neural Network for Trajectory Control of a Robotic Manipulator,” Neural Networks, Vol. 1, pp.251–265, 1988.
R. T. Newton and Y. Xu, “Neural Network Control of a Space Manipulator,” IEEE Control Systems Magazine, Vol. 13, No.6, Decern., pp.14–22, 1993.
D.E. Rumelhart, G.E. Hiton, and J.L. McClelland, “A General Framework for Parallel Distributed Processing,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol.1, D. E. Rumelhart and J. L. McClelland, Eds., Cambridge, MA: MIT Press, pp.45–76, 1986.
D.E. Rumelhart, G. E. Hiton, and R. J. Williams, “Learning Internal Representations by Error Propagation,” in Parallel Distributed Processing: Explorations in the Micro structure of Cognition, vol.1, D. E. Rumelhart and J. L. McClelland, Eds., Cambridge, MA: MIT Press, pp.282–317, 1986.
R. E. Nordgren and P. H. Meckl, “An Analytical Comparison of a Neural Network and a Model-Based Adaptive Controller,” IEEE Trans. on Neural Networks, Vol. 4, No.4, July, pp.685–694, 1993.
M.A. Sartori and P.J. Antsaklis, “Implementation of Learning Control Systems Using Neural Networks,” IEEE Control Systems Magazine, Vol.12, No.2, April, pp.49–57, 1992.
R.M. Sanner and D.L. Akin, “Neuromorphic Pitch Attitude Regulation of an Underwater Telerobot,” IEEE Control System Magazine, Vol.10, No.2, April, pp.62–67, 1990.
W.T. Miller, R.S. Sutton, and P.J. Werbos, “Neural Networks for Control ” The MIT Press, Cambridge, MA, 1990.
S.-B. Yu and S.-R. Hu, “Neural Network For Ship Recognition, ” Proceedings of the International Conference on Fuzzy Logic and Neural Networks, Iizuka, Japan, July 20-24, pp.325–328, 1990.
D. Psaltis, A. Sideris, and A.A. Yamamura, “A Multilayered Neural Network Controller, ” IEEE Control Systems Magazine, Vol.8, No.2, April, pp. 17–20, 1988.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1995 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Teshnehlab, M., Watanabe, K. (1995). Control Strategy of Robotic Manipulator Based on Flexible Neural Network Structure. In: Tzafestas, S.G., Verbruggen, H.B. (eds) Artificial Intelligence in Industrial Decision Making, Control and Automation. Microprocessor-Based and Intelligent Systems Engineering, vol 14. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-0305-3_12
Download citation
DOI: https://doi.org/10.1007/978-94-011-0305-3_12
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-010-4134-8
Online ISBN: 978-94-011-0305-3
eBook Packages: Springer Book Archive