A new approach based on recurrent neural networks for system identification

KALINLI A., Sagiroglu Ş.

COMPUTER AND INFORMATION SCIENCES - ISCIS 2003, cilt.2869, ss.568-575, 2003 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 2869
  • Basım Tarihi: 2003
  • Doi Numarası: 10.1007/978-3-540-39737-3_71
  • Dergi Adı: COMPUTER AND INFORMATION SCIENCES - ISCIS 2003
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, EMBASE, MathSciNet, Philosopher's Index, zbMATH
  • Sayfa Sayıları: ss.568-575
  • Gazi Üniversitesi Adresli: Hayır

Özet

This paper introduces a new approach based on artificial neural networks (ANNs) to identify a number of linear dynamic systems with single recurrent neural model. The structure of single neural model is capable of dealing with systems up to a given maximum number. Single recurrent neural model is trained by the backpropagation with momentum. Total nine systems from first to third orders have been used to validate the approach presented in this work. The results have shown that the recurrent single neural model is capable of identifying a number of systems successfully. The approach presented in this work provides simplicity, accuracy and compactness to system identification.