Abstract
Structure and function of a small but effective neural network controlling the behavior of an autonomous miniatur robot is analyzed. The controller was developed with the help of an evolutionary algorithm, and it uses recurrent connectivity structure allowing non-trivial dynamical effects. The interplay of three different hysteresis elements leading to a skilled behavior of the robot in challenging environments is explicitly discussed.
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© 2002 Springer-Verlag Berlin Heidelberg
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Hülse, M., Pasemann, F. (2002). Dynamical Neural Schmitt Trigger for Robot Control. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_127
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DOI: https://doi.org/10.1007/3-540-46084-5_127
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Print ISBN: 978-3-540-44074-1
Online ISBN: 978-3-540-46084-8
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