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Mathematical Problems in Engineering
Volume 2014, Article ID 764857, 8 pages
http://dx.doi.org/10.1155/2014/764857
Research Article

Grey Accumulation Generation Relational Analysis Model for Nonequidistance Unequal-Length Sequences and Its Application

1College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2College of Science, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Received 24 March 2014; Revised 2 July 2014; Accepted 13 July 2014; Published 22 July 2014

Academic Editor: Seungik Baek

Copyright © 2014 Xuemei Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

As research is required on nonequidistance unequal-length sequences, so grey accumulation generation relational analysis model based on grey exponential law (AGRA) for nonequidistance unequal-length (NDUL) sequences is put forward in this paper. The original data is accumulated generation firstly and the generation sequences are simulated. Then the generation rate is established as the ratio of the tangent slope and the mean of the simulation function. Furthermore, the dynamic similarity of change trend of the original time sequences is characterized by the proximity of generation rate sequences. Meanwhile, properties of AGRA model for nonequidistance unequal-length sequences are discussed. The new relational analysis model is available for equal interval sequences, nonequidistance sequences, sequences which have relationship before transformation and sequences which have relationship after accumulation; therefore, the AGRA model has expanded the scope of application of grey relational analysis. Lastly, factors which affect the amount of passenger cars in China are sorted using AGRA model for NDUL sequences. This application is presented to illustrate the effectiveness and practicality of the proposed model.