Abstract : This paper presents a multi-map recurrent neural architecture, exhibiting self-organization to deal with the partial observations of the phase of some dynamical system. The architecture captures the dynamics of the system by building up a representation of its phases, coping with ambiguity when distinct phases provide identical observations. The architecture updates the resulted representation to adapt to changes in its dynamics due to self-organization property. Experiments illustrate the dynamics of the architecture when fulfilling this goal.
https://hal-supelec.archives-ouvertes.fr/hal-00652312
Contributor : Sébastien van Luchene
<>
Submitted on : Thursday, December 15, 2011 - 11:52:43 AM
Last modification on : Wednesday, July 31, 2019 - 4:18:03 PM
Long-term archiving on : Friday, November 16, 2012 - 3:40:18 PM
