Araştırma Makalesi
BibTex RIS Kaynak Göster

An Analysis of the Effects of SVM Parameters on the Dead-Time System Modeling

Yıl 2018, Cilt: 18 Sayı: 1, 1 - 5, 23.02.2018

Öz

Modeling a dead-time
system is a common issue in engineering applications. To address this issue,
existing research has employed neural networks and fuzzy logic-based intelligent
systems. Herein, a dead-time system modeled with the aid of support vector
machine regression, which has a good generalization feature, was investigated.
The performance of the method proposed herein was examined with different
parameters in linear and nonlinear dead-time systems.

Kaynakça

  • 1. V. N. Vapnik. “Statistical Learning Theory”, John Wiley and Sons, New York, 1998. 2. V. N. Vapnik. “The Nature of Statistical Learning Theory”, Springer-Verlag. New York, 1995. 3. C. Junli, J. Licheng, “Classification Mechanism of Support Vector Machines”, IEEE Proceedings of ICSP p. 1556-1559, 2000. 4. A. Smola, B. Scholkopf, “A Tutorial on Support Vector Regression”, NeuroCOLT2 Technical Report NC-TR pp. 98-30, 1998. 5. L. Guohai, Z. Dawei, X. Haixia, M. Congli “Soft sensor modeling using SVM in fermentation process”, Chinese Journal of Scientific Instrument, vol. 6, 2009. 6. V. Kecman, “Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models”, Cambridge, Mass.: MIT Press, 2001. 7. C. Campbell, “Kernel Methods: A Survey of Current Techniques”, Neurocomputing vol. 48, pp. 63-84, 2002. 8. Z. Hongdong, S. Huihe, “A Novel Method of Process Dead-Time Identification: Support Vector Machine Approach”, Proceeding of American Control Conference, pp. 880- 884, 2004. 9. G. Yan, C. Li, G. Ma, “Parameter selection for support vector machines based on hybrid genetic algorithms”, Journal of Harbin Institute of Technology, vol.5, 2008. 10. H. Zhang, X. Wang, C. Zhang, X. Xu, “Modeling Nonlinear Dynamical Systems Using Support Vector Machine”, Proceeding of 4. International Conf. On Machine Learning and Cybernetics, pp. 3204-3209, 2005. 11. J. Ma, J. Theiler, S. Perkins, “Accurate on-line support vector regression”, Neural Computation, vol.15, pp. 2683-2703, 2003. 12. http://www.sciencedirect.com/science/article/pii/S1226086X13003742 - !A. M. Ghaedi, M. Hossainpour, A. Ansari, M.H. Habibi, A.R. Asghari “Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon”Journal of Industrial and Engineering Chemistry, Volume 20, Issue 4, 25 July 2014, pp. 1641-1649.
Yıl 2018, Cilt: 18 Sayı: 1, 1 - 5, 23.02.2018

Öz

Kaynakça

  • 1. V. N. Vapnik. “Statistical Learning Theory”, John Wiley and Sons, New York, 1998. 2. V. N. Vapnik. “The Nature of Statistical Learning Theory”, Springer-Verlag. New York, 1995. 3. C. Junli, J. Licheng, “Classification Mechanism of Support Vector Machines”, IEEE Proceedings of ICSP p. 1556-1559, 2000. 4. A. Smola, B. Scholkopf, “A Tutorial on Support Vector Regression”, NeuroCOLT2 Technical Report NC-TR pp. 98-30, 1998. 5. L. Guohai, Z. Dawei, X. Haixia, M. Congli “Soft sensor modeling using SVM in fermentation process”, Chinese Journal of Scientific Instrument, vol. 6, 2009. 6. V. Kecman, “Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models”, Cambridge, Mass.: MIT Press, 2001. 7. C. Campbell, “Kernel Methods: A Survey of Current Techniques”, Neurocomputing vol. 48, pp. 63-84, 2002. 8. Z. Hongdong, S. Huihe, “A Novel Method of Process Dead-Time Identification: Support Vector Machine Approach”, Proceeding of American Control Conference, pp. 880- 884, 2004. 9. G. Yan, C. Li, G. Ma, “Parameter selection for support vector machines based on hybrid genetic algorithms”, Journal of Harbin Institute of Technology, vol.5, 2008. 10. H. Zhang, X. Wang, C. Zhang, X. Xu, “Modeling Nonlinear Dynamical Systems Using Support Vector Machine”, Proceeding of 4. International Conf. On Machine Learning and Cybernetics, pp. 3204-3209, 2005. 11. J. Ma, J. Theiler, S. Perkins, “Accurate on-line support vector regression”, Neural Computation, vol.15, pp. 2683-2703, 2003. 12. http://www.sciencedirect.com/science/article/pii/S1226086X13003742 - !A. M. Ghaedi, M. Hossainpour, A. Ansari, M.H. Habibi, A.R. Asghari “Least square-support vector (LS-SVM) method for modeling of methylene blue dye adsorption using copper oxide loaded on activated carbon”Journal of Industrial and Engineering Chemistry, Volume 20, Issue 4, 25 July 2014, pp. 1641-1649.
Toplam 1 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Nihat Kabaoğlu

Rana Ortaç Kabaoğlu Bu kişi benim

Yayımlanma Tarihi 23 Şubat 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 18 Sayı: 1

Kaynak Göster

APA Kabaoğlu, N., & Ortaç Kabaoğlu, R. (2018). An Analysis of the Effects of SVM Parameters on the Dead-Time System Modeling. Electrica, 18(1), 1-5.
AMA Kabaoğlu N, Ortaç Kabaoğlu R. An Analysis of the Effects of SVM Parameters on the Dead-Time System Modeling. Electrica. Şubat 2018;18(1):1-5.
Chicago Kabaoğlu, Nihat, ve Rana Ortaç Kabaoğlu. “An Analysis of the Effects of SVM Parameters on the Dead-Time System Modeling”. Electrica 18, sy. 1 (Şubat 2018): 1-5.
EndNote Kabaoğlu N, Ortaç Kabaoğlu R (01 Şubat 2018) An Analysis of the Effects of SVM Parameters on the Dead-Time System Modeling. Electrica 18 1 1–5.
IEEE N. Kabaoğlu ve R. Ortaç Kabaoğlu, “An Analysis of the Effects of SVM Parameters on the Dead-Time System Modeling”, Electrica, c. 18, sy. 1, ss. 1–5, 2018.
ISNAD Kabaoğlu, Nihat - Ortaç Kabaoğlu, Rana. “An Analysis of the Effects of SVM Parameters on the Dead-Time System Modeling”. Electrica 18/1 (Şubat 2018), 1-5.
JAMA Kabaoğlu N, Ortaç Kabaoğlu R. An Analysis of the Effects of SVM Parameters on the Dead-Time System Modeling. Electrica. 2018;18:1–5.
MLA Kabaoğlu, Nihat ve Rana Ortaç Kabaoğlu. “An Analysis of the Effects of SVM Parameters on the Dead-Time System Modeling”. Electrica, c. 18, sy. 1, 2018, ss. 1-5.
Vancouver Kabaoğlu N, Ortaç Kabaoğlu R. An Analysis of the Effects of SVM Parameters on the Dead-Time System Modeling. Electrica. 2018;18(1):1-5.