Stability analysis of recurrent neural networks with piecewise constant argument of generalized type

AKHMET M., ARUĞASLAN ÇİNÇİN D., YILMAZ E.

NEURAL NETWORKS, cilt.23, sa.7, ss.805-811, 2010 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 23 Sayı: 7
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1016/j.neunet.2010.05.006
  • Dergi Adı: NEURAL NETWORKS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.805-811
  • Anahtar Kelimeler: Neural networks, Piecewise constant argument of generalized type, Method of Lyapunov functions, GLOBAL EXPONENTIAL STABILITY, TIME-VARYING DELAYS, DIFFERENTIAL-EQUATIONS, ASYMPTOTIC STABILITY, PERIODIC-SOLUTIONS, TRAVELING-WAVES
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

In this paper, we apply the method of Lyapunov functions for differential equations with piecewise constant argument of generalized type to a model of recurrent neural networks (RNNs). The model involves both advanced and delayed arguments. Sufficient conditions are obtained for global exponential stability of the equilibrium point. Examples with numerical simulations are presented to illustrate the results. (C) 2010 Elsevier Ltd. All rights reserved.