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In this paper, the sampled-data control is applied to synchronize chaotic neural networks subject to actuator saturation. By employing a time-dependent Lyapunov functional that captures the characteristic information of actual sampling pattern, we derive a local stability condition for the synchronization error systems. By this condition, we design a sampled-data controller to regionally synchronize the drive neural networks and response neural networks subject to actuator saturation. Moreover, an optimization method is given to design the desired sampled-data controller such that the set of admissible initial conditions is maximized. A numerical example is given to demonstrate the effectiveness and merits of the proposed design technique. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of delay-dependent dissipativity analysis for a class of neural networks with time-varying delays. A free-matrix-based inequality method is developed by introducing a set of slack variables, which can be optimized via existing convex optimization algorithms. Then, by employing Lyapunov functional approach, sufficient conditions are derived to guarantee that the considered neural networks are strictly ( Q , S , R ) - γ -dissipative. The conditions are presented in terms of linear matrix inequalities and can be readily checked and solved. Numerical examples are finally provided to demonstrate the effectiveness and advantages of the proposed new design techniques. [ABSTRACT FROM AUTHOR]

This paper focuses on the delay-dependent stability of a class of generalized neural networks (NNs) with time-varying delays. A free-matrix-based inequality is presented by introducing a set of slack variables, which encompasses the Wirtinger-based inequality as a special case. Then, by constructing a suitable Lyapunov–Krasovskii functional and utilizing the new inequality to bound the derivative of the Lyapunov–Krasovskii functional, some sufficient conditions are derived to assure the stability of the considered neural networks. Three numerical examples are provided to demonstrate the effectiveness and the significant improvement of the proposed method. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of passivity of neural networks in the presence of discrete and distributed delay. By constructing an augmented Lyapunov functional and combining a new integral inequality with the reciprocally convex approach to estimate the derivative of the Lyapunov–Krasovskii functional, sufficient conditions are established to ensure the passivity of the considered neural networks, in which some useful information on the neuron activation function ignored in the existing literature is taken into account. Three numerical examples are provided to demonstrate the effectiveness and the merits of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract: This paper deals with the problem of passivity analysis for neural networks with both time-varying delay and norm-bounded parameter uncertainties by employing an improved free-weighting matrix approach. Some useful terms have been retained, which were used to be ignored in the derivative of Lyapunov–Krasovskii functional. Furthermore, the relationship among the time-varying delay, its upper bound and their difference is taken into account. As a result, for two types of time-varying delays, less conservative delay-dependent passivity conditions are obtained in terms of linear matrix inequalities (LMIs), respectively. Finally, a numerical example is given to demonstrate the effectiveness of the proposed techniques. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the problem of absolute stability of Lur׳e systems with time-varying delay and sector-bounded nonlinearity. An improved free-matrix-based inequality (FMBI) is derived. By using this inequality and the convex combination technique, some new delay-dependent absolute stability criteria are derived. These conditions are given in the term of linear matrix inequalities (LMIs) and accordingly can be readily solved and checked. Finally, a numerical example is solved using the proposed method to demonstrate the effectiveness and its improvement over existing ones. [ABSTRACT FROM AUTHOR]

This paper investigates containment for linear multi-agent systems (LMAS) with exogenous disturbances under fixed topologies. Both state feedback and output feedback control protocols are proposed, under which all the followers will asymptotically converge to the convex hull spanned by the leaders. Using tools from matrix, graph and Lyapunov stability theories, sufficient conditions for containment of linear multi-agent systems with exogenous disturbances are obtained by designing exogenous disturbance observers. Finally, numerical simulations are presented to illustrate the theoretical findings. [ABSTRACT FROM AUTHOR]