Temperature Distributions Comparison by the Clustering of Their Proximity Measure

Temperature Distributions Comparison by the Clustering of Their Proximity Measure

Détails bibliographiques
Parent link:Control and Communications (SIBCON): Proceedings of the XII International Siberian Conference, Moscow, May 12–14, 2016. [5 p.].— , 2016
Collectivité auteur: Национальный исследовательский Томский политехнический университет (ТПУ) Институт кибернетики (ИК) Кафедра информатики и проектирования систем (ИПС)
Autres auteurs: Nemirovskiy V. B. Viktor Borisovich, Stoyanov A. K. Aleksandr Kirillovich, Gofman A. K. Aleksandr Konstantinovich, Tartakovsky V. A. Valery Abramovich
Résumé:Title screen
In this paper the method for determining ofproximity of the temperature measurements fields is described.The method includes a clustering of values of the measurementsfields, an estimationof their informational proximity to thechosen reference field by Kullback-Leibler distances and aclustering of thevalues of these distances.Clustering is performedby recurrent neural network. The results of applying theproposed method to the analysis of temperature fields in theNorthern hemisphere for the period of 1955-2010 are presented.
Langue:anglais
Publié: 2016
Collection:Computer Measurement Technologies
Sujets:
электронный ресурс
труды учёных ТПУ
кластеризация
одномерные отображения
нейронные сети
поля
измерения
температура
Accès en ligne:http://dx.doi.org/10.1109/SIBCON.2016.7491730
Format: Électronique Chapitre de livre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=648812

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