Narrow Results

In the letter, the global asymptotic stability of bidirectional associative memory (BAM) neural networks with delays is investigated. The delay is assumed to be time-varying and belongs to a given interval. A novel stability criterion for the stability is presented based on the Lyapunov method. The criterion is represented in terms of linear matrix inequality (LMI), which can be solved easily by various optimization algorithms. Two numerical examples are illustrated to show the effectiveness of our new result.

In this paper, the problem of adaptive ℋ_∞ synchronization for unified chaotic systems with unknown parameter and external disturbance is studied. It is noticed that this unified chaotic system contains the noted Lorentz, Lü and Chen systems. Based on Lyapunov theory and linear matrix inequality (LMI) formulation, the novel feedback controller with adaptive law is established to not only guarantee stable synchronization of both master and slave systems but also reduce the effect of external disturbance to an ℋ_∞ norm constraint. A criterion for existence of the controller is given in terms of LMIs. Finally, a numerical example shows the effectiveness of the proposed method.

This letter considers uncertain Lur'e systems of neutral type with sector and slope restrictions. By constructing a new Lyapunov functional, a novel delay-dependent criterion for absolute stability is derived in terms of linear matrix inequalities (LMIs). Two numerical examples are illustrated to show the effectiveness of the proposed method.

In this letter, the problem of feedback controller design to achieve synchronization for neural network of neutral type with stochastic perturbation is considered. Based on Lyapunov method and LMI (linear matrix inequality) framework, the goal of this letter is to derive an existence criterion of the controller for the synchronization between master and response networks.

In this paper, the problem of stability analysis of neural networks with discrete time-varying delays is considered. By constructing a new Lyapunov functional and some novel analysis techniques, new delay-dependent criteria for checking the asymptotic stability of the neural networks are established. The criteria are presented in terms of linear matrix inequalities, which can be easily solved and checked by various convex optimization algorithms. Three numerical examples are included to show the superiority of our results.