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Neural Computing and Applications

Erschienen in:

01.10.2014 | Original Article

Non-homogenous discrete grey model with fractional-order accumulation

verfasst von: Li-Feng Wu, Si-Feng Liu, Wei Cui, Ding-Lin Liu, Tian-Xiang Yao

Erschienen in: Neural Computing and Applications | Ausgabe 5/2014

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Abstract

It is proved that the non-homogenous discrete grey model (abbreviated as NDGM) with first accumulated generating operator violates the principle of new information priority and principle of minimal information of grey system theory. A new NDGM with the fractional-order accumulation is put forward. The first value is effective when the accumulation order number is not 1, and the priority of new information can be better reflected when the accumulation order number becomes smaller. Three real case studies show that the proposed grey model has higher performances not only on model fitting but also on forecasting.

Vorheriger Artikel Improved robust stability criteria for bidirectional associative memory neural networks under parameter uncertainties

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Titel: Non-homogenous discrete grey model with fractional-order accumulation
verfasst von: Li-Feng Wu
Si-Feng Liu
Wei Cui
Ding-Lin Liu
Tian-Xiang Yao
Publikationsdatum: 01.10.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 5/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1605-1

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