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Hebbian multilayer network in a wheelchair robot

  • Part V: Robotics, Adaptive Autonomous Agents, and Control
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Artificial Neural Networks — ICANN'97 (ICANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

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Abstract

Motivation to study Hebbian learning arises from its neurophysiological plausibility and its suitability for hardware implementation. Up to now, artificial Hebbian learning, embedded in a real system that performs adaptive motor control, has been restricted to one-layer networks. To overcome this limitation, a novel approach to adaptive preprocessing based on Hebbian learning is presented. It is shown how this network is integrated in an adaptive motor control system inspired by classical and operant conditioning models. Experimental results with a real mobile robot are described.

This work is supported through the DFG.

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References

  1. A. Bühlmeier, G. Manteuffel, M. Rossmann, and K. Goser. Robot learning in analog neural hardware. In ICANN 96, pages 311-316. Springer Verlag, 1996.

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Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

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© 1997 Springer-Verlag Berlin Heidelberg

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Bühlmeier, A., Steinen, P., Rossmann, M., Goser, K., Manteuffel, G. (1997). Hebbian multilayer network in a wheelchair robot. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020240

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  • DOI: https://doi.org/10.1007/BFb0020240

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

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