A new upper bound for the norm of interval matrices with application to robust stability analysis of delayed neural networks
Yükleniyor...
Dosyalar
Tarih
2013-08
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Pergamon-Elsevier Science Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
The main problem with the analysis of robust stability of neural networks is to find the upper bound norm for the intervalized interconnection matrices of neural networks. In the previous literature, the major three upper bound norms for the intervalized interconnection matrices have been reported and they have been successfully applied to derive new sufficient conditions for robust stability of delayed neural networks. One of the main contributions of this paper will be the derivation of a new upper bound for the norm of the intervalized interconnection matrices of neural networks. Then, by exploiting this new upper bound norm of interval matrices and using stability theory of Lyapunov functionals and the theory of homomorphic mapping, we will obtain new sufficient conditions for 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. The results obtained in this paper will be shown to be new and they can be considered alternative results to previously published corresponding results. We also give some illustrative and comparative numerical examples to demonstrate the effectiveness and applicability of the proposed robust stability condition.
Açıklama
Anahtar Kelimeler
Interval matrices, Robust stability, Delayed neural networks, Lyapunov functionals, Homomorphic mapping, Discrete-time delays, Distributed delays, Exponential stability, Varying delays, Lmi approach, Neutral-type
Kaynak
Neural Networks
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
44
Sayı
Künye
Faydasıçok, Ö. & Arik, S. (2013). A new upper bound for the norm of interval matrices with application to robust stability analysis of delayed neural networks. Neural Networks, 44, 64-71. doi:10.1016/j.neunet.2013.03.014