19 research outputs found
Generalized non-autonomous Cohen-Grossberg neural network model
In the present paper, we investigate both the global exponential stability
and the existence of a periodic solution of a general differential equation
with unbounded distributed delays. The main stability criterion depends on the
dominance of the non-delay terms over the delay terms. The criterion for the
existence of a periodic solution is obtained with the application of the
coincide degree theorem. We use the main results to get criteria for the
existence and global exponential stability of periodic solutions of a
generalized higher-order periodic Cohen-Grossberg neural network model with
discrete-time varying delays and infinite distributed delays. Additionally, we
provide a comparison with the results in the literature and a numerical
simulation to illustrate the effectiveness of some of our results.Comment: 30 page
Global exponential stability of nonautonomous neural network models with unbounded delays
For a nonautonomous class of n-dimensional di erential system with in nite delays, we give
su cient conditions for its global exponential stability, without showing the existence of an
equilibrium point, or a periodic solution, or an almost periodic solution. We apply our main
result to several concrete neural network models, studied in the literature, and a comparison of
results is given. Contrary to usual in the literature about neural networks, the assumption of
bounded coe cients is not need to obtain the global exponential stability. Finally, we present
numerical examples to illustrate the e ectiveness of our results.The paper was supported by the Research Center of Mathematics of University of Minho with the Portuguese Funds from the FCT - “Fundação para a Ciência e a Tecnologia”, through the Project UID/MAT/00013/2013. The author thanks the referees for valuable comments.info:eu-repo/semantics/publishedVersio
Global stability of Clifford-valued Takagi-Sugeno fuzzy neural networks with time-varying delays and impulses
summary:In this study, we consider the Takagi-Sugeno (T-S) fuzzy model to examine the global asymptotic stability of Clifford-valued neural networks with time-varying delays and impulses. In order to achieve the global asymptotic stability criteria, we design a general network model that includes quaternion-, complex-, and real-valued networks as special cases. First, we decompose the -dimensional Clifford-valued neural network into -dimensional real-valued counterparts in order to solve the noncommutativity of Clifford numbers multiplication. Then, we prove the new global asymptotic stability criteria by constructing an appropriate Lyapunov-Krasovskii functionals (LKFs) and employing Jensen's integral inequality together with the reciprocal convex combination method. All the results are proven using linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the effectiveness of the achieved results