Forecasting Global Temperature Variations by Neural Networks

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/7208

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DC Field	Value	Language
dc.creator	Miyano, Takaya	-
dc.creator	Girosi, Federico	-
dc.date	2004-10-20T20:49:51Z	-
dc.date	2004-10-20T20:49:51Z	-
dc.date	1994-08-01	-
dc.date.accessioned	2013-10-09T02:48:33Z	-
dc.date.available	2013-10-09T02:48:33Z	-
dc.date.issued	2013-10-09	-
dc.identifier	AIM-1447	-
dc.identifier	CBCL-101	-
dc.identifier	http://hdl.handle.net/1721.1/7208	-
dc.identifier.uri	http://koha.mediu.edu.my:8181/xmlui/handle/1721	-
dc.description	Global temperature variations between 1861 and 1984 are forecast usingsregularization networks, multilayer perceptrons and linearsautoregression. The regularization network, optimized by stochasticsgradient descent associated with colored noise, gives the bestsforecasts. For all the models, prediction errors noticeably increasesafter 1965. These results are consistent with the hypothesis that thesclimate dynamics is characterized by low-dimensional chaos and thatsthe it may have changed at some point after 1965, which is alsosconsistent with the recent idea of climate change.s	-
dc.format	11 p.	-
dc.format	342101 bytes	-
dc.format	403018 bytes	-
dc.format	application/octet-stream	-
dc.format	application/pdf	-
dc.language	en_US	-
dc.relation	AIM-1447	-
dc.relation	CBCL-101	-
dc.subject	time series prediction	-
dc.subject	chaotic systems	-
dc.subject	neural nets	-
dc.subject	RBF	-
dc.title	Forecasting Global Temperature Variations by Neural Networks	-
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