Learning and Self-organization for Spatiotemporal Systems
| dc.contributor.author | Runkler, Thomas A. | |
| dc.contributor.author | Sollacher, Rudolf | |
| dc.contributor.author | Szabo, Andrei | |
| dc.date.accessioned | 2018-01-08T09:15:58Z | |
| dc.date.available | 2018-01-08T09:15:58Z | |
| dc.date.issued | 2012 | |
| dc.description.abstract | This article deals with the modeling and management of spatiotemporal systems using machine learning and self-organization algorithms. Two application examples are the localization of objects from radio measurements using spatiotemporal models learned from data, and the self-organizing management of wireless multi-hop sensor networks. For both examples we show how machine learning and self-organization significantly increases accuracy and efficiency. | |
| dc.identifier.pissn | 1610-1987 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/11291 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | KI - Künstliche Intelligenz: Vol. 26, No. 3 | |
| dc.relation.ispartofseries | KI - Künstliche Intelligenz | |
| dc.title | Learning and Self-organization for Spatiotemporal Systems | |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 274 | |
| gi.citation.startPage | 269 |