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dc.contributor.authorYang, Shih-Hungen_US
dc.contributor.authorChen, Yon-Pingen_US
dc.date.accessioned2014-12-08T15:25:17Z-
dc.date.available2014-12-08T15:25:17Z-
dc.date.issued2009en_US
dc.identifier.isbn978-1-4244-2852-6en_US
dc.identifier.urihttp://hdl.handle.net/11536/17669-
dc.identifier.urihttp://dx.doi.org/10.1109/AIM.2009.5229929en_US
dc.description.abstractThis paper presents the design issues of two intelligent forecasting systems, feedforward-neural-network-aided grey model (FNAGM) and Elman-network-aided grey model (ENAGM). Both he FNAGM and ENAGM combine a first-order single variable grey model (GM(1,1)) and a neural network (NN). The GM(1,1) is adopted to predict signal, and the feedforward NN and the Elman network in the FNAGM and ENAGM respectively are used to learn the prediction error of the GM(1,1). Simulation results demonstrate that the intelligent forecasting systems with on-line learning can improve the prediction of the GM(1,1) and can be implemented in real-time prediction.en_US
dc.language.isoen_USen_US
dc.titleIntelligent Forecasting System Based on Grey Model and Neural Networken_US
dc.typeProceedings Paperen_US
dc.identifier.doi10.1109/AIM.2009.5229929en_US
dc.identifier.journal2009 IEEE/ASME INTERNATIONAL CONFERENCE ON ADVANCED INTELLIGENT MECHATRONICS, VOLS 1-3en_US
dc.citation.spage699en_US
dc.citation.epage704en_US
dc.contributor.department電控工程研究所zh_TW
dc.contributor.departmentInstitute of Electrical and Control Engineeringen_US
dc.identifier.wosnumberWOS:000277062800119-
Appears in Collections:Conferences Paper


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