Stability Analysis of A Class of Takagı-Sugeno Fuzzy Cohen-Grossberg Neural Networks with Time Delays

Yıl 2018, Cilt: 23 Sayı: 2, 45 - 54, 31.08.2018

Neyir Özcan Semerci Samet Barış

https://doi.org/10.17482/uumfd.406443

Öz

This paper deals with the problem of the global
asymptotic stability of the class of Takagi-Sugeno Fuzzy Cohen-Grossberg neural
networks with multiple time delays. By constructing a suitable fuzzy Lyapunov
functional, we present a new delay-independent sufficient condition for the
global asymptotic stability of the equilibrium point for delayed Takagi-Sugeno
Fuzzy Cohen-Grossberg neural networks with respect to the Lipschitz activation
functions. The obtained condition simply relies on the network parameters of the
neural system. Therefore, the equilibrium and stability properties of the
neural network model considered in this paper can be easily verified by
exploiting some basic properties of some certain classes of matrices.

Anahtar Kelimeler

T-S Fuzzy Neural Networks, Delayed Systems, Lyapunov Functionals

TAKAGİ-SUGENO BULANIK COHEN-GROSSBERG TİPİ ZAMAN GECİKMELİ YAPAY SİNİR AĞLARINDA KARARLILIK ANALİZİ

Öz

Bu çalışma çoklu zaman gecikmeli Takagi-Sugeno Bulanık
Cohen-Grossberg tipi yapay sinir ağlarının global asimtotik kararlılık problemi
ile ilgilenmektedir. Uygun bulanık Lyapunov fonksiyonelleri kullanılarak ve
aktivasyon fonksiyonlarının Lipschitz olduğu dikkate alnarak, gecikmeli Takagi-Sugeno
Bulanık Cohen-Grossberg yapay sinir ağlarında denge noktasının global asimtotik
gecikme parametrelerinden bağımsız olarak, yeni yeterli bir kararlılık koşulu
sunulmuştur. Elde edilen koşul sadece sinir ağının sistem parametrelerine bağlı
olarak ifade edilmiştir. Bu nedenle, bu çalışmada çalışılan  yapay sinir ağı modelinin denge ve kararlılık
özellikleri, bazı özel matris sınıflarının  temel özellikleri kullanarak kolaylıkla
doğrulanabilir.

Anahtar Kelimeler

T-S Bulanık Sinir Ağları, Gecikmeli Sistemler, Lyapunov Fonksiyonelleri

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