Research Article | Open Access

Volume 2014 |Article ID 764857 | https://doi.org/10.1155/2014/764857

Xuemei Li, Yaoguo Dang, Song Ding, Juan Zhang, "Grey Accumulation Generation Relational Analysis Model for Nonequidistance Unequal-Length Sequences and Its Application", Mathematical Problems in Engineering, vol. 2014, Article ID 764857, 8 pages, 2014. https://doi.org/10.1155/2014/764857

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

Revised02 Jul 2014
Accepted13 Jul 2014
Published22 Jul 2014

#### 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.

#### 1. Introduction

Grey relational analysis (GRA) has emerged as an effective model to measure the degree of correlation between sequences by calculating their similarity or proximity. Some research on GRA and its application has been done, such as the improvement and development of the existing GRA , extension of objects of studying , parallelism and consistency , consistency for affine transformation , and similarity and proximity of GRA .

GRA has been employed in various fields where they have produced promising results due to not limited data distribution and quantity. On the theoretical side, GRA plays its advantage during combination of clustering , decision making , and evaluating . There are also a range of practical or industrial applications employing GRA, for instance, in the period of Chinese regional economic discrepancy , affecting factors of service brand , company financial performance , software effort estimation, energy consumption , Chinese economic development and energy consumption , residential energy consumption and renewable energy utilization , sustainability indicator of renewable energy system , maximum peak discharge at upper stream , and track association algorithm for distributed multitarget system . As a summary, Liu et al. review the research approaches of GRA modeling systematically . Yin conducts a bibliometric study on publication and citation patterns of GRA published from 1996 to 2010 through a systemic search using the ISI web-based databases; results demonstrate that there has been a substantial increase in the number of peer-reviewed papers on GRA indexed by the ISI Web of Knowledge .

From the view of grey systems, although the objective system looks complex, it always has the overall function and certain inherent law. There is a method called grey generation which can explore the real law of the original data. Accumulation generation is an effective kind of grey generation, which whitens grey process and inherent development tendency of accumulation process. Original data may have no regular raw seemingly but emerge integral characteristics by accumulation generation . Because the economic system, ecological system, and agricultural system could be treated as generalized energy system, and the accumulation of energy follows the exponential rule, so accumulation generation has extensive adaptability and has been applied into grey forecasting models. Grey forecasting models based on grey exponential law are employed in a range of applications .

The existing GRA model examines geometric similarity or proximity of data directly or after dimensionless without considering other kinds of law, and research on GRA for nonequidistance unequal-length (NDUL) sequences is required, so AGRA for NDUL sequences based on grey exponential law will be put forward in this paper. Generation rate for NDUL sequences will be established. Then the dynamic change trend similarity of the original time series will be characterized by the proximity of generation rate sequence. Properties of AGRA for NDUL sequences will also be studied. Lastly, an application will be researched using the model proposed in this paper.

The paper is structured as follows. In the next section, AGRA for NDUL sequences is provided. Properties of AGRA for NDUL sequences are researched in Section 3. An application using the model is furnished in Section 4. In Section 5, appropriate conclusions are drawn.

#### 2. Establishment of Grey Generation Rate Relational Analysis Model for Nonequidistance Unequal-Length Sequences

##### 2.1. Simulation of Accumulation Generation Sequence of Nonequidistance Unequal-Length Sequences

Definition 1. Let be a time sequence, where ; then is a nonequidistance sequence.

Definition 2. Let and be time sequences, and ; then and are unequal-length sequences.

Definition 3. Let be the sequence of 1-AGO (one-step accumulating generation operator), where are generated from .

The background value optimized nonequidistance GM (1,1) model in the literature  will be used to simulate accumulation generation sequences. The differential equation is

The discrete form is If is a sequence of parameters and

Then the parameters of the model based on the least squares method are

The solution, also known as time response function, of the whitenization equation is given by where

Its discrete form is

##### 2.2. Generation of Equidistance Equal-Length Sequences from Nonequidistance Unequal-Length Sequences

Suppose that and are nonnegative sequences, and ; then equidistance equal-length sequences and can be obtained by time response equation in Section 2.1 based on and . The time intervals of new sequences are one and lengths are .

##### 2.3. Establishment of Grey Generation Rate Relational Analysis Model Based on Generated Sequences

The slope of the tangent of at time can be got as (9) by the function of and the meaning of slope:

The mean value of between can be calculated as

The slope is the measure of the tilt of the linear. The slope of the tangent of at time is the derivative value of at time , which is the change rate of at time . Then the slope of the tangent is divided by the mean value to establish the generation rate which eliminates the order of magnitudes.

Definition 4. Generation rate of at time is defined as (11), where :

Definition 5. Let and be generation rates of and at time ; then is the generation rate sequence of system characteristic sequence and are the generation rate sequences of system behavioral sequence , where

Definition 6. is the nonnegative system characteristic sequence and are nonnegative system behavioral sequence sequences. Equidistance equal-length sequences and are generated from and according to Section 2.1. Then, is named as the grey relational coefficient of AGRA between and at time .

reflects the proximity of change trend at time between and , so the closer development tendency at time of and is, the smaller is, meanwhile, the greater the relational coefficient of and is, and vice versa. The constriction of function for the relational coefficient is consistent with the meaning of grey relational analysis.

Definition 7. Grey relational degree of AGRA between and is constructed as

The grey relational coefficient of AGRA between sequences reflects the degree of consensus of sequences with change trend at one moment, and the grey relational degree of AGRA between sequences is the mean of the grey relational coefficient, so the grey relational degree of AGRA between sequences is measurement of consensus of sequences with development tendency for the whole time.

##### 2.4. The Step of AGRA Model for NDUL Sequences

The steps to obtain the order of sequences by AGRA model for NDUL sequences are concluded as follows.(1)Make qualitative analysis on system characteristics sequence to confirm system behavioral sequences .(2)Get the accumulation generation sequences of and by (1).(3)Obtain the fitting curve functions of generation sequences according to (4)–(8).(4)Construct the generation rate sequences using (9)–(11).(5)Calculate the grey relational coefficients of AGRA between and at time () by (13).(6)Determine the grey relational degree between and based on grey relational coefficients and (14). Confirm the order of behavioral sequences between characteristics sequence according to the grey relational degree of AGRA.

#### 3. Properties of AGRA Model for NDUL Sequences

Theorem 8. AGRA model for NDUL sequences has the following properties.(1). , iff for .(2)Uniqueness and Independence. is unique for fixed two sequences and is not influenced by other sequences.(3)Proximity. The closer the development tendency of sequences is, the greater is.(4)Transitivity. If and , then .

Proof. , , where ; then
There is no sense to research correlation between these sequences. So .
As , , so ; that is , and ; therefore
If , then ; if , , then .
According to the definition of the grey relational coefficient and grey relational degree of AGRA, is unique for fixed and and is not influenced by other sequences.
The closer change trends at time of and is, the smaller is. meanwhile, the greater the relational coefficient of and is, and vice versa. The closer development tendency of whole and is, the greater is.
Transitivity can be known by Properties 2 and 3.

Theorem 9. Let , is the system characteristics sequence, are the behavioral sequences, and equidistance equal-length sequences based on and are and , , . The grey relational degree of AGRA will remain the same after and are divided by the initial value or the mean value, or multiplied; in other words, the grey relational degree of AGRA will not change by dimensionless of sequences.

Proof. The simulation functions of and which are the accumulation generation sequences of and , , are
Then the generation rates of and at time are Now let , , and , are the accumulation generation sequences of and . The simulation functions of and are Then the generation rates after the dimensionless of dates are Meanwhile, , , , can be got by Li .
So, , ; then
Therefore, the grey relational coefficients of generation rate between and will remain the same after and are divided by the initial value or the mean value, or multiplied; in other words, the grey relational coefficients of AGRA will not be affected by dimensionless so do the grey relational degree of AGRA. Another conclusion can be got; the AGRA model for NDUL sequences is suitable for both positive sequences and negative sequences.

Theorem 10. Let behavioral sequences be , and , , ; then .

Proof. The generation rates of and at time are
As , , so , , then ; therefore So

Theorem 11. The AGRA model for NDUL sequences has noise reduction effect and is stable for noise.

Proof. As initial sequences in AGRA model for NDUL sequences are accumulation generated before taken into the model, and accumulation generation has noise reduction effect, so the model reduces the impact of noise. Stability and validity for noise interference in this model will be demonstrated as follows.
Let be the sequence which has noise, the sequence without noise, and the noise; then As can be ignored when compared with and , and the derivative of is infinitesimal, so Therefore, the AGRA model for NDUL sequences is stable for noise.

#### 4. Case Study

Traffic jam is becoming one of the most difficult problems in which cities are facing as the urban space is expanding and the population is accumulating. Traffic accidents, air pollution, noise pollution, resource shortages, and other issues which are brought by traffic jam are hindering the global economy and even threatening human survival; that is why traffic jam is one of the most focused on problems for academic organizations and governments. It is significant to sort factors which affect the amount of commercial passenger cars for traffic management and congestion controlling. The road mileage, turnover passengers on highway, and the number of civil aircrafts are selected as affecting factors according to the existing literatures and expert advices. The original data of affecting factors are in Table 1.

 2005 2006 2007 2008 2009 2010 2011 2012 Commercial passenger cars (ten thousand) 2132.46 3838.92 4845.09 6124.13 7478.37 8943.01 Road mileage (ten thousand of kilometers) 334.52 345.70 358.37 373.02 386.08 400.82 410.64 423.75 Turnover passengers on highway (Million passenger-kilometer) 9292.08 10130.85 11506.77 12476.11 13511.44 15020.81 16760.25 18467.55 The number of civil aircrafts 1386 1961 2181 2405 3191 3589
Date resource: 2013 China's Transportation Almanac.

Due to the limitation of data source, the numbers of commercial passenger cars and civil aircrafts in 2006 and 2007 are lacking. Common GRA models fail to handle this situation. In order to make full use of existing data, the AGRA model for NDUL sequences is required to rank factors which affect the number of commercial passenger cars. The parameters during the mathematical calculation are provided in Table 2. The grey generation rate degrees of incidence and the ranking list are in Table 3.

 0 1 2 3 The parameters in simulation functions −0.14017 −0.03390 −0.09820 −0.09141 3403.67530 330.23450 8799.98560 1793.96982 The generation rates of sequences 0.179630 0.209493 0.184575 0.195901 0.206659 0.216716 0.203620 0.214652 0.237754 0.224189 0.224630 0.235198 0.273529 0.231919 0.247808 0.257710 0.314686 0.239916 0.273378 0.282377 0.362037 0.248189 0.301586 0.309405 0.416512 0.256747 0.332705 0.339020 0.479184 0.265600 0.367034 0.371470 The grey relational coefficients of AGRA 0.85745 0.97321 0.91694 0.85745 0.95359 0.98551 0.96276 0.95359 0.94602 0.94769 0.98936 0.94602 0.86796 0.91405 0.94533 0.86796 0.80801 0.88396 0.90689 0.80801 0.76077 0.85692 0.87308 0.76077 0.72276 0.83249 0.84313 0.72276 0.69169 0.81034 0.81647 0.69169
 Road mileage Turnover passengers on highway Civil aircrafts The grey relational degrees of AGRA 0.82603 0.90052 0.90674 The racking 3 2 1

As shown in Table 3, the number of civil aircrafts is more affected by requirement of society than social capacity. The driving action of requirement of society is greater than the restriction of social capacity in front of the increasing of cars.

#### 5. Conclutions and Future Studies

AGRA model for NDUL sequences is established in this paper. The original data is accumulated firstly and the curve is fitted by optimized GM (1,1) model for nonequidistance sequences; then the fitting equation is obtained. Generation rate is established as the ratio of the tangent slope and the mean of the fitting equation. The similarity of dynamic change trend of the original time series is characterized by the proximity of generation sequences. The AGRA model for NDUL sequences is available for both equal interval sequences and nonequidistance sequences. Meanwhile, the new model can handle sequences which have relationship before transformation and sequences which have relationship after accumulation. Accumulation generation reflects development tendency of data and expands the scope of application of grey relational analysis. Meanwhile, properties of AGRA model for NDUL sequences are researched. It is shown that AGRA model for NDUL sequences is not limited to positive sequences. In the last section, factors which affect the number of passenger cars in China are ordered using the model proposed in this paper. It is illustrated that the proposed model is effective and practical.

Because the economic system, ecological system, and agricultural system could be treated as generalized energy system, the accumulation of energy follows the exponential rule which is used to establish AGRA model. Meanwhile, data missing is common in practical problem so that NDUL sequences are general. In conclusion, AGRA model for NDUL sequences can be applied but not limited to economic system, ecological system, and agricultural system problems with NDUL sequences.

There are some potential studies that can be done in the future according to the ideas presented in this paper, including conducting a new AGRA model based on other kinds of GRA models; in this way, more applications of other kinds of AGRA models can be determined. The AGRA model for NDUL sequences can also be combined with clustering methods and decision making methods in the presence of NDUL sequences.

#### Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

#### Acknowledgments

The authors would like to express their gratitude to the editor and anonymous reviewers for their helpful comments which improved the quality of the paper. The paper is sponsored by National Natural Science Foundation of China (71071077, 71371098); Fundamental Research Funds for the Central Universities (NC2012001, NZ2010006); Funding of Jiangsu Base of Major Projects on Philosophy and Social Sciences (2012JDXM005); Funding of Jiangsu Innovation Program for Graduate Education (CXZZ12_0174); and Funding for Outstanding Doctoral Dissertation in NUAA (BCXJ12-11).

1. H. Y. Du, H. X. Shi, S. F. Liu et al., “Study on the model of grey periodic incidence judged on slope,” Chinese Journal of Management Science, vol. 18, no. 1, pp. 128–132, 2010. View at: Google Scholar
2. H. J. Xiong, M. Y. Chen, and T. Qu, “Some extensions of grey relational grade formula,” Systems Engineering and Electronics, vol. 22, no. 1, pp. 8–10, 2000. View at: Google Scholar
3. Y. G. Dang, S. F. Liu, B. Liu et al., “Improvement on degree of grey slope incidence,” Engineering Science, vol. 6, no. 3, pp. 41–44, 2004. View at: Google Scholar
4. Y. G. Sun and Y. G. Dang, “Improved model of slope grey relational analysis,” Statistics and Decision, vol. 15, pp. 12–13, 2007. View at: Google Scholar
5. Y. Sun and Y. Dang, “Improvement on grey T's correlation degree,” System Engineering Theory & Practice, vol. 28, no. 4, pp. 135–139, 2008. View at: Google Scholar
6. K. Zhang and S. Liu, “Extended clusters of grey incidences for panel data and its application,” System Engineering—Theory & Practice, vol. 30, no. 7, pp. 1253–1259, 2010. View at: Google Scholar
7. N. M. Xie and S. F. Liu, “The parallel and uniform properties of several relational models,” Systems Engineering, vol. 25, no. 8, pp. 98–103, 2007. View at: Google Scholar
8. J. Cui, Y. G. Dang, and S. F. Liu, “Novel properties of some grey incidence analysis models,” Systems Engineering, vol. 27, no. 4, pp. 65–70, 2009. View at: Google Scholar
9. S. F. Liu, N. M. Xie, and J. Forrest, “On new models of grey incidence analysis based on visual angle of similarity and nearness,” System Engineering Theory and Practice, vol. 30, no. 5, pp. 881–887, 2010. View at: Google Scholar
10. K. Guo and Q. Zhang, “Spectral clustering based on grey relational analysis,” System Engineering Theory and Practice, vol. 30, no. 7, pp. 1260–1265, 2010. View at: Google Scholar
11. G. W. Wei and Y. Wei, “Model of grey relational analysis for interval multiple attribute decision making with preference information on alternatives,” Chinese Journal of Management Science, vol. 16, no. 1, pp. 158–162, 2008. View at: Google Scholar
12. Y. H. Wang and Y. G. Dang, “Method of grey fixed weight clustering comprehensive ex-post evaluation based on D-S evidential theory,” System Engineering: Theory and Practice, vol. 29, no. 5, pp. 128–134, 2009. View at: Google Scholar
13. J. J. Jin, C. M. Mi, W. X. Xu et al., “The maximum entropy empowerment model for evaluating index considering the expert evaluation information,” Chinese Journal of Management Science, vol. 20, no. 2, pp. 135–143, 2012. View at: Google Scholar
14. G. Wei, “Gray relational analysis method for intuitionistic fuzzy multiple attribute decision making,” Expert Systems with Applications, vol. 38, no. 9, pp. 11671–11677, 2011. View at: Publisher Site | Google Scholar
15. J. H. Jung and W. T. Kwon, “Optimization of EDM process for multiple performance characteristics using Taguchi method and Grey relational analysis,” Journal of Mechanical Science and Technology, vol. 24, no. 5, pp. 1083–1090, 2010. View at: Publisher Site | Google Scholar
16. Y. Wang, “Analysis of FDI and Chinese regional economic discrepancy using grey incidence theory,” System Engineering Theory & Practice, vol. 30, no. 3, pp. 426–430, 2010. View at: Google Scholar
17. H. Y. Wei, L. Zhang, Y. M. Liang et al., “Research on the effect of multidimensional interaction on service brand equity-based gray relational analysis,” Journal of Management Science in Chinese, vol. 14, no. 10, pp. 43–53, 2011. View at: Google Scholar
18. C. Kung and K. Wen, “Applyinggrey relational analysis and grey decision-making to evaluate the relationship between company attributes and its financial performance-a case study of venture capital enterprises in Taiwan,” Decision Support Systems, vol. 43, no. 3, pp. 843–852, 2007. View at: Publisher Site | Google Scholar
19. S. Huang, N. Chiu, and L. Chen, “Integration of the grey relational analysis with genetic algorithm for software effort estimation,” European Journal of Operational Research, vol. 188, no. 3, pp. 898–909, 2008.
20. C. Yuan, S. Liu, Z. Fang, and N. Xie, “The relation between Chinese economic development and energy consumption in the different periods,” Energy Policy, vol. 38, no. 9, pp. 5189–5198, 2010. View at: Publisher Site | Google Scholar
21. Z. Ma, H. Wang, A. Wu et al., “An intelligent decision support system for residential energy consumption and renewable energy utilization in rural China,” Energy Sources B—Economics Planning and Policy, vol. 9, no. 4, pp. 374–382, 2014. View at: Google Scholar
22. G. Liu, A. M. Baniyounes, M. G. Rasul, M. T. O. Amanullah, and M. M. K. Khan, “General sustainability indicator of renewable energy system based on grey relational analysis,” International Journal of Energy Research, vol. 37, no. 14, pp. 1928–1936, 2013. View at: Publisher Site | Google Scholar
23. Y. M. Wang, G. S. Chen, and Z. L. Yan, “Law of annual maximum peak discharge at upper stream of the yellow river,” Journal of Water Resources & Water Engineering, vol. 34, no. 2, pp. 80–82, 2013. View at: Google Scholar
24. X. Yi, H. Zhang, X. Cao, and Y. He, “A track association algorithm for distributed multi-target systems based on gray interval numbers,” Acta Aeronautica et Astronautica Sinica, vol. 34, no. 2, pp. 352–360, 2013. View at: Publisher Site | Google Scholar
25. S. Liu, Y. Yang, Y. Cao et al., “A summary on the research of GRA models,” Grey Systems: Theory and Application, vol. 3, no. 1, pp. 7–15, 2013. View at: Google Scholar
26. M. Yin, “Fifteen years of grey system theory research: a historical review and bibliometric analysis,” Expert Systems with Applications, vol. 40, no. 7, pp. 2767–2775, 2013. View at: Publisher Site | Google Scholar
27. S. F. Liu, Y. G. Dang, Z. G. Fang et al., Grey System Theory and Application, Science Press, Beijing, China, 2012.
28. R. B. Carmona Benítez, R. B. Carmona Paredes, G. Lodewijks, and J. L. Nabais, “Damp trend Grey Model forecasting method for airline industry,” Expert Systems with Applications, vol. 40, no. 12, pp. 4915–4921, 2013. View at: Publisher Site | Google Scholar
29. W. Zhou and J.-M. He, “Generalized GM (1, 1) model and its application in forecasting of fuel production,” Applied Mathematical Modelling, vol. 37, no. 9, pp. 6234–6243, 2013. View at: Publisher Site | Google Scholar | MathSciNet
30. H. J. Liu, P. Han, and D. F. Wang, “Fuzzy PID control with grey prediction and its application in temperature control system,” Journal of System Simulation, vol. 16, no. 8, pp. 1839–1841, 2004. View at: Google Scholar
31. X. C. Li, “Widening of suitable limits of grey system GM (1,1) model,” Systems Engineering—Theory & Practice, vol. 19, no. 1, pp. 98–102, 1999. View at: Google Scholar