title: Analysis of motor control and behavior in multi agent systems by means of artificial neural networks.
abstract: This article gives a short introduction to Self-Organizing Maps, a particular form of Artificial Neural Networks and shows by some examples, how these approaches can be used in order to analyze and visualize time series data originating from complex systems. The methods shown in this article have originally been developed for the analysis of RoboCup robot soccer games, a special kind of so-called Multi Agent Systems. Although this application has no direct connection to biomechanics, the examples shown here may give an impression of the abilities of Neural Networks in the field of Time Series Analysis in general. Because of the abstractness of the methods, it appears to be very likely that they can easily be adapted to Time Series Analysis problems within the biomechanics context.
authors.affiliation: Department of Computer Science, Johannes Gutenberg University, 55099 Mainz, Germany.
authors.forename: Thomas
authors.initials: T
authors.lastname: Uthmann
authors.forename: Peter
authors.initials: P
authors.lastname: Dauscher
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008959:Models, Neurological; D009043:Motor Activity / Q000502:physiology*; D009068:Movement / Q000502:physiology*; D016571:Neural Networks, Computer; D012371:Robotics / Q000379:methods*