| dc.contributor.author | Reinhart, René Felix | |
| dc.date | 2012-11-01 | |
| dc.date.accessioned | 2018-01-08T09:16:10Z | |
| dc.date.available | 2018-01-08T09:16:10Z | |
| dc.date.issued | 2012 | |
| dc.identifier.issn | 1610-1987 | |
| dc.identifier.uri | http://dl.gi.de/handle/20.500.12116/11323 | |
| dc.description.abstract | This thesis presents a dynamical system approach to learning forward and inverse models in associative recurrent neural networks. Ambiguous inverse models are represented by multi-stable dynamics. Random projection networks, i.e. reservoirs, together with a rigorous regularization methodology enable robust and efficient training of multi-stable dynamics with application to movement control in robotics. | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 26, No. 4 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.subject | Dynamical systems | |
| dc.subject | Machine learning | |
| dc.title | Reservoir Computing with Output Feedback | |
| dc.type | Text/Journal Article | |
| mci.reference.pages | 415-416 | |
| gi.identifier.doi | 10.1007/s13218-012-0187-2 | |
