KaydedilenlerDerginizi ekleyin

Dergiler / Istanbul University Journal of Electrical and Electronics Engineering / 2011 / Cilt: 11 - Sayı: 1

A support vector regressıon method for reducıng the hıgh-order systems to fırst-order plus tıme-delay forms

Rana ORTAÇ KABADĞLU
Sayfa
1305–1309
Yayın Tarihi
2022-10-18
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Satisfactory performance is obtained at the simulations.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In this paper, a novel method for reducing the high-order systems to first-order plus time-delay forms is introduced.
📊
Temel Bulgular & Veri:For this purpose Support Vector Machines, which became a popular learning algorithm, is employed.
💡
Sonuç & Katkı:Satisfactory performance is obtained at the simulations.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

In this paper, a novel method for reducing the high-order systems to first-order plus time-delay forms is introduced. For this purpose Support Vector Machines, which became a popular learning algorithm, is employed. Three parameters of the first-order plus time-delay forms are estimated by three parallel support vector regression machines. Satisfactory performance is obtained at the simulations.

🔖

Bu Makaleye Atıf Yap

KABADĞLU, R. O. (2022). A support vector regressıon method for reducıng the hıgh-order systems to fırst-order plus tıme-delay forms. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 11 - Sayı: 1, 1305–1309. https://ajindex.com/articles/543697

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (13)

  1. J.P., Richard, “Time Delay Systems: An Overview of Some Recent Advances and Open Problems”, Automatica, Vol: 39, Issue: 10, pp. 1667-1694, 2003
  2. S.V., Drakunov, W., Perruquetti, J.P., Richard, “Delay Identification in Time-Delay Systems Using Variable Structure Observers”, Annual Reviews in Control, Vol: 30, Issue: 2, pp. 143-158, 2006
  3. V., Venkatashankar, M., Chidambaram, “Design of P and stPI Controllers for Unstable 1 Order Plus Time-Delay Systems”, International Journal of Control, Vol: 60, No:1, pp.137-144, 1994
  4. S., Tavakoli, M., Tavakoli, “Optimal Tuning of PID Controllers for First Order Plus Time Delay Models Using Dimensional Analysis”, The fourth International Conference on Control and Automation (ICCA’03), Montreal, Canada, 2003
  5. S., Skogestad, “Simple Analytic Rules for Model Reduction and PID Controller Tuning”, Journal of Process Control, Vol: 13, pp.291-309, 2003
  6. V.N., Vapnik “The Nature of Statistical Learning Theory”, Springer-Verlag, New York. 1995
  7. V.N., Vapnik “Statistical Learning Theory”, John Wiley and Sons, New York. 1998
  8. N., Cristianini, J., Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods”, Cambridge University Press, Cambridge, 2000
  9. L, Zhao, J, Sun, KR. Butts, “Linear programming support vector regression with wavelet kernel: A new approach to nonlinear dynamical systems identification”, Mathematics and Computers in Simulation,2009;79:2051–2063
  10. S., Iplikci, “A support vector machine based control application to the experimental three-tank system”, ISA Transactions 2010;49:376-386
  11. A.J., Smola, B., Schoelkopf, “A tutorial on support vector regression”, NeuroCOLT2 Technical Report NC- TR-98-030, Royal Holloway College, University of London, UK. 1998
  12. J., Ma, J., Theiler, S., Perkins, “Accurate on-line support vector regression”, Neural Computation, vol.15, p: 2683-2703, 2003
  13. 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, p:3204-3209, 2005.