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Identification of nonlinear dynamical systems using multilayer perceptrons

Doğrusal olamayan devingen düzgelerin çok tabakalı algılayıcılar kullanılarak tanıyımı

  1. Tez No: 23508
  2. Yazar: TANSEL VOYVODAOĞLU
  3. Danışmanlar: PROF. DR. MÜBECCEL DEMİREKLER
  4. Tez Türü: Yüksek Lisans
  5. Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
  6. Anahtar Kelimeler: Neural networks, multUayer perceptrons, system identification, back propagation, learning rate, induction generator, Neural networks, multUayer perceptrons, system identification, back propagation, learning rate, induction generator
  7. Yıl: 1992
  8. Dil: İngilizce
  9. Üniversite: Orta Doğu Teknik Üniversitesi
  10. Enstitü: Fen Bilimleri Enstitüsü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 128

Özet

ABSTRACT IDENTIFICATION OF NONLINEAR DYNAMICAL SYSTEMS USING MULTILAYER PERCEPTRONS VOYVODAOĞLU, Tansel M.S. 1n Electrical and Electronics Engineerlng Supervisor: Prof.Dr. Mübeccel Demirekler September, 1992, 115 pages in thls study, the dynamical systems represented by nonlinear dlfference eguations are identlfied by uslng neural networks. Back propagation tralning algorithm 1s used to train multilayer perceptrons for the representatlon of nonlinear functions. W1thin this scope, neural networks and back propagation trainlng algorithm are critically rev1ewed especially from system 1dentificat1on point of view. Furthermore, computer simulatlon tests are carrled out by using the proposed 1dent1fication models to demonstrate the important propertles of the algorithm. Apart from those, different versions of the back propagation training algorithm are used in identification of the slmpUfied model of a wind türbine driven self-excited induction generator. The slmulation results reveal that multilayer perceptrons trained by back propagation method can be used effectively in 11irepresentation of nonlinear functlons in finite intervals. However, it 1s also observed that back propagatlon tralning algorithm has a very s1ow convergence rate and does not a!ways guarantee the global minimum of the performance measure.

Özet (Çeviri)

ABSTRACT IDENTIFICATION OF NONLINEAR DYNAMICAL SYSTEMS USING MULTILAYER PERCEPTRONS VOYVODAOĞLU, Tansel M.S. 1n Electrical and Electronics Engineerlng Supervisor: Prof.Dr. Mübeccel Demirekler September, 1992, 115 pages in thls study, the dynamical systems represented by nonlinear dlfference eguations are identlfied by uslng neural networks. Back propagation tralning algorithm 1s used to train multilayer perceptrons for the representatlon of nonlinear functions. W1thin this scope, neural networks and back propagation trainlng algorithm are critically rev1ewed especially from system 1dentificat1on point of view. Furthermore, computer simulatlon tests are carrled out by using the proposed 1dent1fication models to demonstrate the important propertles of the algorithm. Apart from those, different versions of the back propagation training algorithm are used in identification of the slmpUfied model of a wind türbine driven self-excited induction generator. The slmulation results reveal that multilayer perceptrons trained by back propagation method can be used effectively in 11irepresentation of nonlinear functlons in finite intervals. However, it 1s also observed that back propagatlon tralning algorithm has a very s1ow convergence rate and does not a!ways guarantee the global minimum of the performance measure.

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