Fuzzy time series analysis and forecasting

Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/76763

DC Field	Value	Language
dc.contributor	應數系	-
dc.creator	Wu, Berlin	-
dc.creator	吳柏林	zh_TW
dc.creator	Nguyen, H.T.	en_US
dc.date	2006	-
dc.date.accessioned	2015-07-21T07:29:34Z	-
dc.date.available	2015-07-21T07:29:34Z	-
dc.date.issued	2015-07-21T07:29:34Z	-
dc.identifier.uri	http://nccur.lib.nccu.edu.tw/handle/140.119/76763	-
dc.description.abstract	The problem of system modeling and identification has attracted considerable attention during the past decades mostly because of a large number of applications in diverse fields. In this chapter we give a well defined fuzzy time series model with forecasting through the Markov fuzzy relation. An illustrative algorithm based on a fuzzy logic is developed to predicate the multivariate dynamic data. © Springer-Verlag Berlin Heidelberg 2006.	-
dc.format.extent	176 bytes	-
dc.format.mimetype	text/html	-
dc.relation	Studies in Fuzziness and Soft Computing, 198, 145-182	-
dc.title	Fuzzy time series analysis and forecasting	-
dc.type	article	en
dc.identifier.doi	10.1007/11353492_9	-
dc.doi.uri	http://dx.doi.org/10.1007/11353492_9	-
item.grantfulltext	restricted	-
item.openairecristype	http://purl.org/coar/resource_type/c_18cf	-
item.fulltext	With Fulltext	-
item.openairetype	article	-
item.cerifentitytype	Publications	-
Appears in Collections:	期刊論文

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