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.
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Acknowledgments
The authors are Grateful to anonymous referees for their helpful and constructive comments on this paper. The relevant researches done are supported by a Marie Curie International Incoming Fellowship within the 7th European Community Framework Programme (Grant No.FP7-PIIF-GA-2013-629051), National Natural Science Foundation of China (Nos.71171113, 71171116), Research Fund for the Doctoral Program of Higher Education (No.2013218120036), Project of Social Science Foundation of the China (No.12AZD102), Natural Science Foundation of Jiangsu Province (No.BK20130786).
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Wu, LF., Liu, SF., Cui, W. et al. Non-homogenous discrete grey model with fractional-order accumulation. Neural Comput & Applic 25, 1215–1221 (2014). https://doi.org/10.1007/s00521-014-1605-1
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DOI: https://doi.org/10.1007/s00521-014-1605-1