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This paper deals with global synchronization in arrays of coupled delayed neural networks with delayed coupling. Through employing Lyapunov-Krasovskii functional and Kronecker product technique, one novel synchronization criterion is presented in terms of linear matrix inequalities(LMIs) based on the integral inequality and convex combination, in which the condition is dependent not only on lower...
In this paper, based on Lyapunov-Krasovskii functional approach and proper integral inequality, one novel sufficient condition is derived to guarantee the global stability for neural networks with interval time-varying delay, in which the general convex combination is employed. The LMI-based criterion heavily depends on the upper and lower bounds on both time delay and its derivative, which is different...
This paper investigates the robust exponential stability for discrete-time Cohen-Grossberg neural networks with both time-varying and distributed delays. By constructing a novel Lyapunov-Krasovskii functional and introducing some free-weighting matrices, two delay-dependent sufficient conditions are obtained by using convex combination. These criteria are presented in terms of LMIs and their feasibility...
In this paper, an augmented Lyapunov functional, which takes an integral term of state vector into account, is introduced. Owing to the functional, an improved delay-dependent asymptotic stability criterion for delay neural networks is derived in term of LMIs. Moreover, the result is also extended to rate-independent stability criteria for unknown time-varying delay. Finally, numerical examples are...
In this letter, simplified delay-dependent stability criteria for neural networks are derived by using a simple integral inequality. The results are in terms of linear matrix inequalities (LMIs) and turn out to be equivalent to some existing results but include less number of LMI variables. This implies that some redundant variables in the existing stability criteria can be removed while maintaining...
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