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

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...

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Main Authors: Neyir Özcan Semerci̇, Samet Barış
Format: Article
Language:English
Published: Bursa Uludag University 2018-08-01
Series:Uludağ University Journal of The Faculty of Engineering
Subjects:
Online Access:https://dergipark.org.tr/tr/pub/uumfd/issue/36936/406443
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author Neyir Özcan Semerci̇
Samet Barış
author_facet Neyir Özcan Semerci̇
Samet Barış
author_sort Neyir Özcan Semerci̇
collection DOAJ
description 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.
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spelling doaj.art-fefe79cd331341fca87241b395e5672a2023-02-15T16:11:25ZengBursa Uludag UniversityUludağ University Journal of The Faculty of Engineering2148-41472148-41552018-08-01232455410.17482/uumfd.4064431779Stability Analysis of A Class of Takagı-Sugeno Fuzzy Cohen-Grossberg Neural Networks with Time DelaysNeyir Özcan Semerci̇Samet BarışThis 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.https://dergipark.org.tr/tr/pub/uumfd/issue/36936/406443t-s bulanık sinir ağlarıgecikmeli sistemlerlyapunov fonksiyonellerit-s fuzzy neural networksdelayed systemslyapunov functionals
spellingShingle Neyir Özcan Semerci̇
Samet Barış
Stability Analysis of A Class of Takagı-Sugeno Fuzzy Cohen-Grossberg Neural Networks with Time Delays
Uludağ University Journal of The Faculty of Engineering
t-s bulanık sinir ağları
gecikmeli sistemler
lyapunov fonksiyonelleri
t-s fuzzy neural networks
delayed systems
lyapunov functionals
title Stability Analysis of A Class of Takagı-Sugeno Fuzzy Cohen-Grossberg Neural Networks with Time Delays
title_full Stability Analysis of A Class of Takagı-Sugeno Fuzzy Cohen-Grossberg Neural Networks with Time Delays
title_fullStr Stability Analysis of A Class of Takagı-Sugeno Fuzzy Cohen-Grossberg Neural Networks with Time Delays
title_full_unstemmed Stability Analysis of A Class of Takagı-Sugeno Fuzzy Cohen-Grossberg Neural Networks with Time Delays
title_short Stability Analysis of A Class of Takagı-Sugeno Fuzzy Cohen-Grossberg Neural Networks with Time Delays
title_sort stability analysis of a class of takagi sugeno fuzzy cohen grossberg neural networks with time delays
topic t-s bulanık sinir ağları
gecikmeli sistemler
lyapunov fonksiyonelleri
t-s fuzzy neural networks
delayed systems
lyapunov functionals
url https://dergipark.org.tr/tr/pub/uumfd/issue/36936/406443
work_keys_str_mv AT neyirozcansemerci stabilityanalysisofaclassoftakagısugenofuzzycohengrossbergneuralnetworkswithtimedelays
AT sametbarıs stabilityanalysisofaclassoftakagısugenofuzzycohengrossbergneuralnetworkswithtimedelays