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Bağımlı değişkenin simetrik bulanık sayı olması durumunda parametre tahmini

Yıl 2010, Cilt: 3 Sayı: 2, 54 - 62, 30.12.2010

Öz

Bu çalmada, baml deikenin simetrik bulank say olmas durumunda, regresyon modelinin

parametrelerinin tahmin edilmesi için, bulank çkarsama sistemine dayal uyarlamal an (ANFIS)

kullanld bir algoritma ve bulank robust regresyon’a dayal bir algoritma ele alnarak parametre tahmini

yaplmtr. Bulank robust regresyon ve ANFIS’in kullanld algoritmadan elde sonuçlar Dimond (1988)

tarafndan önerilen yöntemden elde edilen sonuçlar ile karlatrlmtr.

Kaynakça

  • D.H. Hong, Hwang, C.,2004, Extended fuzzy regression model using regularization method, Information Science, 164, 31-46.
  • H. Ishibuchi, M. Nei, 2001, Fuzzy Regression using Asymmetric Fuzzy Coefficients and Fuzzied Neural Networks, Fuzzy Sets and Systems, 119, 273–290.
  • D. James, W. Donalt, 1999, Fuzzy Number Neural Networks, Fuzzy Sets and Systems,108, 49-58.
  • S. Jozsef, 1992, On the e_ect of linear data transformation in the possibilistic fuzzy linear regression, Fuzzy Sets and Systems,45, 185-188.
  • J. Jyh-Shing Roger, 1993, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transaction on Systems, Man and Cybernetics, 23(3), 665-685
  • C. Kao, CL. Chyu,2003, Least-squares estimates in fuzzy regression analysis. Eur. J. Operational Res ,148, 426–35.
  • K.S. Kula, A. Apayd n, 2008, Fuzzy Robust Regression Analysis Based on the Ranking of Fuzzy Sets, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 16, 663-681.
  • D. T. Redden, W.H. Woddall,1996, Further examination of fuzzy linear regression, Fuzzy Sets and Systems 79, 203-211.
  • H. Tanaka, S. Uejima, K. Asai, 1982, Linear regression analysis with fuzzy model. IEEE Trans. Systems Man Cybernet, 12, 903-907.
  • H. Tanaka, I. Ishibuchi, 1991, Identification of possibility linear systems by quadratic membership functions of fuzzy parameters, Fuzzy Sets and Systems, 41,145-160.
  • H. Tanaka, 1987, Fuzzy data analysis by possibilistic linear models, Fuzzy Sets and Systems, 24, 363-375.
  • M.S. Yang, H.H. Liu, 2003, Fuzzy least squares algorithms for interactive fuzzy linear regression models. Fuzzy Sets and Systems, 135, 305-316.
  • M.S. Yang, C.H. Ko, 1997, On cluster-wise fuzzy regression analysis. IEE Transaction on Systems, Man and Cybernetics Part B: Cybernetics, 27, 1-13.
  • J. Watada, Y. Yabuuchi,1994, Fuzzy robust regression analysis. Fuzzy/IEEE’94, Theird IEEE International Conference on Fuzzy Systems, 1-7.

Parameter estimation in the case of symmetric fuzzy number dependent variable

Yıl 2010, Cilt: 3 Sayı: 2, 54 - 62, 30.12.2010

Öz

In this study, we will use two algorithms for a regression parameter’s estimation. Based on ANFIS and fuzzy

robust regression model, in cases where dependent variables is a symmetrical fuzzy number. Results taken

from the fuzzy robust regression are based on ANFIS and are compared with the Diamond method.

Kaynakça

  • D.H. Hong, Hwang, C.,2004, Extended fuzzy regression model using regularization method, Information Science, 164, 31-46.
  • H. Ishibuchi, M. Nei, 2001, Fuzzy Regression using Asymmetric Fuzzy Coefficients and Fuzzied Neural Networks, Fuzzy Sets and Systems, 119, 273–290.
  • D. James, W. Donalt, 1999, Fuzzy Number Neural Networks, Fuzzy Sets and Systems,108, 49-58.
  • S. Jozsef, 1992, On the e_ect of linear data transformation in the possibilistic fuzzy linear regression, Fuzzy Sets and Systems,45, 185-188.
  • J. Jyh-Shing Roger, 1993, ANFIS: Adaptive-Network-Based Fuzzy Inference System, IEEE Transaction on Systems, Man and Cybernetics, 23(3), 665-685
  • C. Kao, CL. Chyu,2003, Least-squares estimates in fuzzy regression analysis. Eur. J. Operational Res ,148, 426–35.
  • K.S. Kula, A. Apayd n, 2008, Fuzzy Robust Regression Analysis Based on the Ranking of Fuzzy Sets, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 16, 663-681.
  • D. T. Redden, W.H. Woddall,1996, Further examination of fuzzy linear regression, Fuzzy Sets and Systems 79, 203-211.
  • H. Tanaka, S. Uejima, K. Asai, 1982, Linear regression analysis with fuzzy model. IEEE Trans. Systems Man Cybernet, 12, 903-907.
  • H. Tanaka, I. Ishibuchi, 1991, Identification of possibility linear systems by quadratic membership functions of fuzzy parameters, Fuzzy Sets and Systems, 41,145-160.
  • H. Tanaka, 1987, Fuzzy data analysis by possibilistic linear models, Fuzzy Sets and Systems, 24, 363-375.
  • M.S. Yang, H.H. Liu, 2003, Fuzzy least squares algorithms for interactive fuzzy linear regression models. Fuzzy Sets and Systems, 135, 305-316.
  • M.S. Yang, C.H. Ko, 1997, On cluster-wise fuzzy regression analysis. IEE Transaction on Systems, Man and Cybernetics Part B: Cybernetics, 27, 1-13.
  • J. Watada, Y. Yabuuchi,1994, Fuzzy robust regression analysis. Fuzzy/IEEE’94, Theird IEEE International Conference on Fuzzy Systems, 1-7.
Toplam 14 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

K. Şanlı Kula

Türkan Erbay Dalkılıç

Ayşen Apaydın Bu kişi benim

Yayımlanma Tarihi 30 Aralık 2010
Yayımlandığı Sayı Yıl 2010 Cilt: 3 Sayı: 2

Kaynak Göster

IEEE K. Ş. Kula, T. E. Dalkılıç, ve A. Apaydın, “Bağımlı değişkenin simetrik bulanık sayı olması durumunda parametre tahmini”, JSSA, c. 3, sy. 2, ss. 54–62, 2010.