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Evolved neurocontrollers for pole-balancing

  • Neural Networks for Communications, Control and Robotics
  • Conference paper
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Biological and Artificial Computation: From Neuroscience to Technology (IWANN 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1240))

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Abstract

An evolutionary algorithm for the development of neural networks with arbitrary connectivity is presented. The algorithm is not based on genetic algorithms, but is inspired by a biological theory of coevolving species. It sets no constraints on the number of neurons and the architecture of a network, and develops network topology and parameters like weights and bias terms simultaneously. Designed for generating neuromodules acting in embedded systems like autonomous agents, it can be used also for the evolution of neural networks solving nonlinear control problems. Here we report on a first test, where the algorithm is applied to a standard control problem: the balancing of an inverted pendulum.

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Author information

Authors and Affiliations

  1. Research Center Jülich, IBI 1, D-52425, Jülich, Germany

    Frank Pasemann

  2. International Institute for Applied Systems Analysis, ADN, A-2361, Laxenburg, Austria

    Ulf Dieckmann

Authors
  1. Frank Pasemann
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  2. Ulf Dieckmann
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Editor information

José Mira Roberto Moreno-Díaz Joan Cabestany

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© 1997 Springer-Verlag Berlin Heidelberg

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Pasemann, F., Dieckmann, U. (1997). Evolved neurocontrollers for pole-balancing. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032588

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  • DOI: https://doi.org/10.1007/BFb0032588

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63047-0

  • Online ISBN: 978-3-540-69074-0

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