Search

This paper proposes a new consensus criterion for nonlinear complex systems with edge betweenness centrality measure. By construction of a suitable Lyapunov-Krasovskii functional, the consensus criterion for such systems is established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. One numerical example is given to illustrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of stability analysis for neural networks with time-varying delays. By utilizing the Wirtinger based integral inequality and constructing a suitable augmented Lyapunov-Krasovskii functional, two less conservative delay dependent criteria to guarantee the asymptotic stability of the concerned networks are derived in terms of linear matrix inequalities (LMIs). Three numerical examples are included to explain the superiority of the proposed methods by comparing maximum delay bounds with the recent results published in other literature. [ABSTRACT FROM AUTHOR]

The problem of passivity analysis for neural networks with time-varying delays and parameter uncertainties is considered. By the consideration of newly constructed Lyapunov-Krasovskii functionals, improved sufficient conditions to guarantee the passivity of the concerned networks are proposed with the framework of linear matrix inequalities (LMIs), which can be solved easily by various efficient convex optimization algorithms. The enhancement of the feasible region of the proposed criteria is shown via two numerical examples by the comparison of maximum allowable delay bounds. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of ℋ∞ synchronization for chaotic neural networks with time-varying delays. A new model of the networks with disturbances in both master and slave systems is presented. By constructing a suitable Lyapunov—Krasovskii functional and using a reciprocally convex approach, a novel ℋ∞ synchronization criterion for the networks concerned is established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. Two numerical examples are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This study is concerned with the stability problem for uncertain discrete-time systems with interval time-varying delays. By construction of an augmented Lyapunov-Krasovskii's functional and utilisation of new zero equalities, improved delay-dependent criteria for the stability of the systems are derived for guaranteeing the asymptotic stability of the concerned systems. The effectiveness and the reduced conservatism of the derived results are demonstrated by three illustrative examples. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to investigate the delay-dependent stability analysis for discrete-time neural networks with interval time-varying delays. Based on Lyapunov method, improved delaydependent criteria for the stability of the networks are derived in terms of linear matrix inequalities LMIs by constructing a suitable Lyapunov-Krasovskii functional and utilizing reciprocally convex approach. Also, a new activation condition which has not been considered in the literature is proposed and utilized for derivation of stability criteria. Two numerical examples are given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]