New Criteria for Global Robust Stability of Delayed Neural Networks With Norm-Bounded Uncertainties


Arik S.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, cilt.25, sa.6, ss.1045-1052, 2014 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 25 Konu: 6
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1109/tnnls.2013.2287279
  • Dergi Adı: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
  • Sayfa Sayıları: ss.1045-1052

Özet

In this paper, we study the global asymptotic robust stability of delayed neural networks with norm-bounded uncertainties. By employing the Lyapunov stability theory and homeomorphic mapping theorem, we derive some new types of sufficient conditions ensuring the existence, uniqueness, and global asymptotic stability of the equilibrium point for the class of neural networks with discrete time delays under parameter uncertainties and with respect to continuous and slope-bounded activation functions. An important aspect of our results is their low computational complexity, as the reported results can be verified by checking some properties of symmetric matrices associated with the uncertainty sets of the network parameters. The obtained results are shown to be generalizations of some of the previously published corresponding results. Some comparative numerical examples are also constructed to compare our results with some closely related existing literature results.