Artificial Neural Network approach on Type II Regression Analysis

Abstract

In this study, the Artificial Neural Network (ANN) approach was applied to the OLS-Bisector technique, which is one of the Type II Regression techniques, through this study. In order to measure the performance of this newly created ANN-Bisector technique, it was compared with the OLS-Bisector technique. First of all, literature information on ANN and OLS-Bisector Regression techniques is given, and the features of two techniques are mentioned. In line with this information, a comparison was made between OLS based bisector technique and ANN based bisector techniques. In order to compare these two techniques, they were modeled in different distributions and in different sample sizes. In order to compare the performances of these models, the "Mean Absolute Percent Error" (MAPE) criterion was used. As a result of the study, it was seen that the ANN based bisector technique gave better results with lower error than the OLS based bisector technique. With this study, it is foreseen that it will represent an example for researchers who want to work in these fields in the future.

Keywords

• Artificial Neural Network
• Type II Regression
• Measurement Error Models.

References

1. Aygören, H., Sarıtaş, H., Moralı, T. (2012). İMKB 100 Endeksinin Yapay Sinir Ağları ve Newton Nümerik Arama Modelleri ile Tahmini. Uluslararası Alanya İşletme Fakültesi Dergisi, 73-88.
2. Bayır, F. (2006). Yapay sinir ağları ve tahmin modellemesi üzerine bir uygulama. Yüksek Lisans Tezi, İstanbul Üniversitesi Sosyal Bilimler Enstitüsü. İstanbul.
3. Dawson, C. W., Wilby, R. (1998) An artificial neural network approach to rainfall-runoff modelling. Hydrological Sciences Journal. 43(1), 47-66.
4. Ergezer, H., Dikmen, M., Özdemir, E. (2003). Yapay sinir ağları ve tanıma sistemleri. PIVOLKA, 2(6), 14-17.
5. Hopfield, J. J. (1982) Neural networks and physical systems with emergent collective computational abilities. Proc. Nat. Acad. Sci. 79, 2554-2558.
6. Hopfield, J. J. (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc. Nat. Acad. Sci. 81, 3088-3092.
7. Khan, K., Sahai, A. (2012) A Comparison of BA, GA, PSO, BP and LM for Training Feed forward Neural Networks in e-Learning Context. I.J. Intelligent Systems and Applications, 7, 23-29.
8. Rumelhart, D. E., Hinton, G. E., Williams, R. J. (1986) Learning Internal Representation by Error Propagation. In: Rumelhart DE, McClelland JL and the PDP Research Group (eds) Parallel distributed processing, 1. MIT Press, Cambridge, Mass.

9. Saraçlı, S. (2011). Tip II Regresyon Tekniklerinin Monte-Carlo Simülasyonu ile Karşılaştırılması. e-Journal of New World Sciences Academy, 6(2), 26-35.
10. Suresh, S., Omkar, S. N., Mani, V. (2005) Parallel Implementation of Back-Propagation Algorithm in Networks of Workstations, IEEE Transactions on Parallel And Distributed Systems, 16(1), 24-34.
11. Uğur, A., Kınacı, A. C. (2006). Yapay Zeka Teknikleri ve Yapay Sinir Ağları Kullanılarak Web Sayfalarının Sınıflandırılması . "Türkiye'de İnternet" Konferans Bildirileri. Ankara: TOBB Ekonomi ve Teknoloji Üniversitesi. 345-349.
12. Saraçlı S. (2008). Comparison of Linear Regression Techniques in Measurement Error Models - Monte-Carlo Simulation Study -. Doctoral Dissertation, Eskişehir Osmangazi University, Department of Statistics.
13. Saylor, R.D., Edgerton, E.S. and Hartsell, B.E. (2006) Linear regression techniques for use in the EC tracer method of secondary organic aerosol estimation., Atmospheric Environment 40, 7546 -7556.
14. Isobe, T., Feigelson, E.D., Akritas, M.G. and Babu, G.J. (1990) Linear Regression in Astronomy I, the Astrophysical Journal, 364, 104-113.
15. Matlab User’s Guide, 2020, https://www.mathworks.com/help/deeplearning/ ref/logsig.html# :~:text=logsig%20is%20a%20transfer%20function,output%20from%20its%20net%20input.&text=and%20returns%20A%20%2C%20the%20S,A%20with%20respect%20to%20N%20. Date of Access: 28.10.2020.
16. Tunca, B. (2019) Artificial neural network approach on type II regression analysis. M.Sc. Thesis, Afyon Kocatepe University Institute of Science, Afyonkarahisar.