Multy Variable Grey Method For Multy Point Deformation Analysis

Year 2018, Volume: 13 Issue: 1, 65 - 68, 01.03.2018

Levent Taşçi Erkan Köse

Abstract

Grey theory is one of
the methods used to study uncertainty. The uncertain systems characterized by
small sample and poor information are the study object of grey system theory.
Multivariable grey prediction models are part of grey forecasting system. They
are presented if there are mutual relations among the factors in the system.
They believe that all the influencing factors are not independent of each other
and should be regarded as a whole. In multivariable grey forecasting models,
the future value of a variable is tried to be forecasted considering the other
influential factors in the system. In this study, deformation consisting on the
crest of a Dam is aimed to determine by using multivariable grey prediction
models.

Keywords

Deformation, Grey method, Deformation predict

References

• 1. Deng, J.L. (1982). Control problems of grey systems. S.ystems and Control Letters, 1(5): 211–215. 2. Huang, Y.P. and C.C. Huang, C.C. (1996). The integration and application of fuzzy and grey modeling methods. Fuzzy Sets and Systems, 78(1): 107-119. 3. T. Tien. (2005). The indirect measurement of tensile strength of material by the grey prediction model GMC(1,n). Measurement Science and Technology, 16(6): 1322–1328. 4. Wu, W.Y. and Chen, S.P. (2005). A prediction method using the grey model GMC(1,n) combined with the grey relational analysis a case study on internet access population forecast. Applied Mathematics and Computation, 169(1): 198–217. 5. Hsu, L. (2009). Forecasting the output of integrated circuit industry using genetic algorithm based multivariable grey optimization models. Expert Systems with Applications, 36(4): 7898–7903. 6. Hsu, L. and Wang, C. (2009). Forecasting integrated circuit output using multivariate grey model and grey relational analysis. Expert Systems with Applications, 36(2): 1403–1409. 7. Luo,Y.X., Wu,X., Li,M. and Cai, A.H. (2009). Grey dynamic model GM(1,N) for the relationship of cost and variability. Kybernetes, 38(3): 435–440. 8. Tien, T.L. (2012) A research on the grey prediction model GM(1,n). Applied Mathematics and Computation, 218(9): 4903–4916. 9. Niu, W., Zhai, Z., Wang, G., Cheng, J. and Guo, Y. (2011). Adaptive multivariable grey prediction model. Journal of Information Computer Science, 8(10): 1801-1808. 10. Hui, H., Li, F., and Shi, Y. (2013). An Optımal Multı-Varıable Grey Model for Logıstıcs Demand Forecast. International Journal of Innovative Computing, Information and Control, 9(7): 2907-2918.