Abstract
Power law tails can be observed in the statistics of human motor control such as the balancing of a stick at the fingertip. We derive a simple control algorithm that employs optimal parameter estimation based on past observations. The resulting control system self-organizes into a critical regime, whereby the exponents of power law tails do not depend on system parameters. The occurrence of power laws is robust with respect to the introduction of delays and a variation in the length of the memory trace. Our results suggest that multiplicative noise causing scaling behavior may result from optimal control.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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Eurich, C.W., Pawelzik, K. (2005). Optimal Control Yields Power Law Behavior. 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_58
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DOI: https://doi.org/10.1007/11550907_58
Publisher Name: Springer, Berlin, Heidelberg
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