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Learning and Self-organization for Spatiotemporal Systems

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2012

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Springer

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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.

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Runkler, Thomas A.; Sollacher, Rudolf; Szabo, Andrei (2012): Learning and Self-organization for Spatiotemporal Systems. KI - Künstliche Intelligenz: Vol. 26, No. 3. Springer. PISSN: 1610-1987. pp. 269-274

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