Learning and Self-organization for Spatiotemporal Systems

被引：0

作者：

Runkler, Thomas A. ^{[1
]}

Sollacher, Rudolf ^{[1
]}

Szabo, Andrei ^{[1
]}

机构：

[1] Siemens AG Corp Technol, D-80200 Munich, Germany

来源：

KUNSTLICHE INTELLIGENZ | 2012年 / 26卷 / 03期

关键词：

D O I：

10.1007/s13218-012-0171-x

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：269 / 274

页数：6

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