Comparison of three-back-propagation training algorithms for two case studies

Kişi Ö., Uncuoglu E.

INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES, cilt.12, sa.5, ss.434-442, 2005 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 12 Sayı: 5
  • Basım Tarihi: 2005
  • Dergi Adı: INDIAN JOURNAL OF ENGINEERING AND MATERIALS SCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.434-442
  • Erciyes Üniversitesi Adresli: Evet

Özet

This paper investigates the use of three back-propagation training algorithms, Levenberg-Marquardt, conjugate gradient and resilient back-propagation, for the two case studies, stream-flow forecasting and determination of lateral stress in cohesionless soils. Several neural network (NN) algorithms have been reported in the literature. They include various representations and architectures and therefore are suitable for different applications. In the present study, three NN algorithms are compared according to their convergence velocities in training and performances in testing. Based on the study and test results, although the Levenberg-Marquardt algorithm has been found being faster and having better performance than the other algorithms in training, the resilient back-propagation algorithm has the best accuracy in testing period.