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Yayın New criteria for the existence of stable equilibrium points in nonsymmetric cellular neural networks(2003) Özcan, Neyir; Arık, Sabri; Tavşanoğlu, Ahmet VedatThis paper presents new criteria for the existence of stable equilibrium points in the total saturation region for cellular neural networks (CNNs). It is shown that the results obtained can be used to derive some complete stability conditions for some special classes of CNNs such as positive cell-linking CNNs, opposite-sign CNNs and dominant-template CNNs. Our results are also compared with the previous results derived in the literature for the existence of stable equilibrium points for CNNs.Yayın New criteria for the existence of stable equilibrium points in nonsymmetric cellular neural networks(IEEE, 2003) Özcan, Neyir; Arık, Sabri; Tavşanoğlu, Ahmet VedatA new criteria for the existence of stable equilibrium points in nonsymmetric cellular neural networks (CNN) was presented. It was shown that the results obtained can be used to derive some complete stability conditions for some special classes of CNNs such as positive cell-linking CNNs, opposite-sign CNNs and dominant-template CNNs. The model of the CNN whose dynamical behavior was described by the state equations was discussed.Yayın Robust stability analysis of a class of delayed neural networks(2012) Özcan, Neyir; Arik, SabriThis paper studies the global robust stability of delayed neural networks. A new sufficient condition that ensures the existence, uniqueness and global robust asymptotic stability of the equilibrium point is presented. The obtained condition is derived by using the Lyapunov stability and Homomorphic mapping theorems and by employing the Lipschitz activation functions. The result presented establishes a relationship between the network parameters of the neural system independently of time delays. We show that our results is new and improves some of the previous global robust stability results expressed for delayed neural networks.Yayın New global robust stability condition for uncertain neural networks with time delays(Elsevier Science BV, 2014-10-22) Özcan, Neyir; Arik, SabriIn this paper, we investigate the robust stability problem for the class of delayed neural networks under parameter uncertainties and with respect to nondecreasing activation functions. Firstly, some new upper bound values for the elements of the intervalized connection matrices are obtained. Then, a new sufficient condition for the existence, uniqueness and global asymptotic stability of the equilibrium point for this class of neural networks is derived by constructing an appropriate Lyapunov-Krasovskii functional and employing homeomorphism mapping theorem. The obtained result establishes a new relationship between the network parameters of the neural system and it is independent of the delay parameters. A comparative numerical example is also given to show the effectiveness, advantages and less conservatism of the proposed result.
