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Abstract: This paper presents the analysis of delay-dependent bounded input bounded output (BIBO) stability for a class of switched uncertain neutral systems. The uncertainty is assumed to be of structured linear fractional form which includes the norm-bounded uncertainty as a special case. First, by introducing the general variation-of-constants formula of neutral systems with perturbation, the BIBO stability property of general linear switched neutral systems with perturbation is established. Next, combining the general variation-of-constants formula with the state-dependent switching rule, new approaches are presented to design the feedback controller and the switching rules. Furthermore, the BIBO stability criteria are obtained in terms of the so-called Lyapunov–Metzler linear matrix inequalities (LMIs). Finally, simulation examples are given to demonstrate the effectiveness and the potential of the proposed techniques in this paper. [Copyright &y& Elsevier]

Abstract: In this paper, the problem on an unique equilibrium solution of the bidirectional associative memory neural networks with distributed time-varying delays is investigated via Lyapunov stability theory. By using topological degree theory and M-matrix theory, several sufficient conditions ensuring the existence, uniqueness and global asymptotic stability of equilibrium solution are derived. An illustrative example is given to demonstrate the effectiveness of the obtained results. [Copyright &y& Elsevier]

Abstract: The stabilization problem for a class of discrete-time systems with time-varying delay is investigated. By constructing an augmented Lyapunov function, some sufficient conditions guaranteeing exponential stabilization are established in forms of linear matrix inequality (LMI) technique. When norm-bounded parameter uncertainties appear in the delayed discrete-time system, a delay-dependent robust exponential stabilization criterion is also presented. All of the criteria obtained in this paper are strict linear matrix inequality conditions, which make the controller gain matrix can be solved directly. Three numerical examples are provided to demonstrate the effectiveness and improvement of the derived results. [Copyright &y& Elsevier]

Abstract: The problem of robust exponential stability for a class of discrete-time recurrent neural networks with time-varying delay is investigated. By constructing a new augmented Lyapunov–Krasovskii functional, some new delay-dependent stable criteria are obtained. These criteria are formulated in the forms of linear matrix inequality (LMI). Compared with some previous results, the new conditions obtained in this paper are less conservative. Three numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed method. [Copyright &y& Elsevier]