Please use this identifier to cite or link to this item: https://ah.lib.nccu.edu.tw/handle/140.119/72854
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dc.contributor應數系-
dc.creatorWu, Berlin-
dc.creator吳柏林-
dc.creatorChen, Shuenn-Renen_US
dc.date2003-09-
dc.date.accessioned2015-01-13T07:43:34Z-
dc.date.available2015-01-13T07:43:34Z-
dc.date.issued2015-01-13T07:43:34Z-
dc.identifier.urihttp://nccur.lib.nccu.edu.tw/handle/140.119/72854-
dc.description.abstractIn this paper we present a new approach on optimal forecasting by using the fuzzy set theory and soft computing methods for the dynamic data analysis. This research is based on the concepts of fuzzy membership function as well as the natural selection of evolution theory. Some discussions in the sensitivity of the design of fuzzy processing will be provided. Through the design of genetic evolution, the AIC criteria is used as the adjust function, and the fuzzy memberships function of each gene model are calculated. Simulation and empirical examples show that our proposed forecasting technique can give an optimal forecasting in time series analysis. [PUBLICATION ABSTRACT]-
dc.format.extent558669 bytes-
dc.format.mimetypeapplication/pdf-
dc.relationFuzzy Optimization and Decision Making, 2(3), 215-228-
dc.subjectoptimal forecasting; soft computation; genetic modeling; membership function-
dc.titleOn Optimal Forecasting with Soft Computation for Nonlinear Time Series-
dc.typearticleen
dc.identifier.doi10.1023/A:1025090420345en_US
dc.doi.urihttp://dx.doi.org/10.1023/A:1025090420345en_US
item.grantfulltextrestricted-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypearticle-
item.cerifentitytypePublications-
item.fulltextWith Fulltext-
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