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ECAL
2005
Springer

(Co)Evolution of (De)Centralized Neural Control for a Gravitationally Driven Machine

13 years 9 months ago
(Co)Evolution of (De)Centralized Neural Control for a Gravitationally Driven Machine
Using decentralized control structures for robot control can offer a lot of advantages, such as less complexity, better fault tolerance and more flexibility. In this paper the evolution of recurrent artificial neural networks as centralized and decentralized control architectures will be demonstrated. Both designs will be analyzed concerning their structure-function relations and robustness against lesion experiments. As an application, a gravitationally driven robotic system will be introduced. Its task can be allocated to a cooperative behavior of five subsystems. A co-evolutionary strategy for generating five autonomous agents in parallel will be described.
Steffen Wischmann, Martin Hülse, Frank Pasema
Added 27 Jun 2010
Updated 27 Jun 2010
Type Conference
Year 2005
Where ECAL
Authors Steffen Wischmann, Martin Hülse, Frank Pasemann
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