Abstract
In terms of computational neuroscience, several theoretical learning schemes have been proposed to acquire suitable motor controllers in the human brain. The controllers have been classified into a feedforward manner and a feedback manner as inverse models of controlled objects. For learning a feedforward controller, we have proposed a forward-propagation learning (FPL) rule which propagates error “forward” in a multi-layered neural network to solve a credit assignment problem. In the current work, FPL is simplified to realize accurate learning, and to be extended to adaptive feedback control. The suitability of a proposed scheme is confirmed by computer simulation.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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.
References
Jordan, M.I., Rumelhert, D.E.: Forward models: Supervised learning with a distal teacher. Cognitive Sciences 16, 307–354 (1992)
Kawato, M.: Feedback-error-learning neural network for supervised motor learning. In: Eckmiller, R. (ed.) Advanced Neural Computers, pp. 365–372. North Holland, Amsterdam (1990)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagation errors. Nature 323, 533–536 (1986)
Nagasawa, K., Fukumura, N., Uno, Y.: A forward-propagation rule for acquiring inverse models in multi-layered neural networks (in Japanese). IEICE Trans. J85-D-II, 1066–1074 (2002)
Ohama, Y., Fukumura, N., Uno, Y.: A forward-propagation rule for acquiring neural inverse models using a RLS algorithm. In: Pal, N.R., et al. (eds.) Neural Information Processing, pp. 585–590. Springer, Berlin (2004)
Ohama, Y., Fukumura, N., Uno, Y.: A forward-propagation learning rule in consideration of the correlation of propagated errors (in Japanese). IEICE Trans. J88-D-II, 218–229 (2005)
Gomi, H., Kawato, M.: Adaptive feedback control models of the vestibulocerebellum and spinocerebellum. Biol. Cybern. 68, 105–114 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ohama, Y., Fukumura, N., Uno, Y. (2005). A Simplified Forward-Propagation Learning Rule Applied to Adaptive Closed-Loop Control. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_69
Download citation
DOI: https://doi.org/10.1007/11550907_69
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28755-1
Online ISBN: 978-3-540-28756-8
eBook Packages: Computer ScienceComputer Science (R0)