Filtreler

2013 - 20143
Filtreleri Sıfırla

Ayarlar

Yazar: Arik, Sabri × Erişim Hakkı: info:eu-repo/semantics/openAccess ×

Arama Sonuçları

Listeleniyor 1 - 3 / 3
  • Küçük Resim
    Yayın
    An analysis of stability of a class of neutral-type neural networks with discrete time delays
    (Hindawi Publishing Corporation, 2013) Orman, Zeynep; Arik, Sabri
    The problem of existence, uniqueness, and global asymptotic stability is considered for the class of neutral-type neural network model with discrete time delays. By employing a suitable Lyapunov functional and using the homeomorphism mapping theorem, we derive some new delay-independent sufficient conditions for the existence, uniqueness, and global asymptotic stability of the equilibrium point for this class of neutral-type systems. The obtained conditions basically establish some norm and matrix inequalities involving the network parameters of the neural system. The main advantage of the proposed results is that they can be expressed in terms of network parameters only. Some comparative examples are also given to compare our results with the previous corresponding results and demonstrate the effectiveness of the results presented.
  • Küçük Resim
    Yayın
    Analysis of Nonlinear Dynamics of Neural Networks
    (Hindawi Publishing Corporation, 2013) Arik, Sabri; Park, Juhyun; Huang, Tingwen; Oliveira, José J
    [No abstract available]
  • Küçük Resim
    Yayın
    Further analysis of stability of uncertain neural networks with multiple time delays
    (Springer International Publishing AG, 2014-01-27) Arik, Sabri
    This paper studies the robust stability of uncertain neural networks with multiple time delays with respect to the class of nondecreasing activation functions. By using the Lyapunov functional and homeomorphism mapping theorems, we derive a new delay-independent sufficient condition the existence, uniqueness, and global asymptotic stability of the equilibrium point for delayed neural networks with uncertain network parameters. The condition obtained for the robust stability establishes a matrix-norm relationship between the network parameters of the neural system, and therefore it can easily be verified. We also present some constructive numerical examples to compare the proposed result with results in the previously published corresponding literature. These comparative examples show that our new condition can be considered as an alternative result to the previous corresponding literature results as it defines a new set of network parameters ensuring the robust stability of delayed neural networks.