Improved result on stability analysis of discrete stochastic neural networks with time delay

This Letter investigates the problem of exponential stability for discrete stochastic time-delay neural networks. By defining a novel Lyapunov functional, an improved delay-dependent exponential stability criterion is established in terms of linear matrix inequality (LMI) approach. Meanwhile, the computational complexity of the newly established stability condition is reduced because less variables are involved. Numerical example is given to illustrate the effectiveness and the benefits of the proposed method.     

Mixed time-delays dependent exponential stability for uncertain stochastic high-order neural networks

This paper presents a discrete and distributed time-delays dependent simultaneous approach to deterministic and uncertain stochastic high-order neural networks. New results are proposed in terms of linear matrix inequality (LMI) by exploiting a novel Lyapunov–Krasovskii functional and by making use of novel techniques for time-delay systems. Some constraints of systems are removed, and new results cover some recently published works. Two numerical examples are given to show the usefulness of presented approach.     

On dynamics analysis of a new chaotic attractor

In this Letter, a new chaotic system is discussed. Some basic dynamical properties, such as Lyapunov exponents, Poincaré mapping, fractal dimension, bifurcation diagram, continuous spectrum and chaotic dynamical behaviors of the new chaotic system are studied, either numerically or analytically. The obtained results show clearly that the system discussed in this Letter is a new chaotic system and deserves a further detailed investigation.     

Generating a new chaotic attractor by feedback controlling method

A new chaotic system is found by feedback controlling method in this paper. According to the definition of the generalized Lorenz system, the new chaotic system does not belong to generalized Lorenz systems. We analyze the new system by means of phase portraits, Lyapunov exponents, fractional dimension, bifurcation diagram, and Poincaré map. The particular interest is that this novel system can generate two one‐scroll and one two‐scroll chaotic attractors with the variation of a single parameter. The obtained results show clearly that the system is a new chaotic system and deserves a further detailed investigation. Copyright © 2011 John Wiley & Sons, Ltd.     

Topology identification of the modified complex dynamical network with non-delayed and delayed coupling

In practical situations, there exists much uncertain information in complex networks, such as the topological structures and the time delays. So the identification of the topology is an important issue in the research of the complex networks with time delays. In this paper, we consider the problem of identification of the topology of modified complex networks with non-delayed and delayed coupling, and achieve the synchronization of the response networks with the drive networks. Finally, some simulation results are given to show the effectiveness of the method proposed in this paper.     

Adaptive lag synchronization and parameters adaptive lag identification of chaotic systems

This Letter investigates the problem of adaptive lag synchronization and parameters adaptive lag identification of chaotic systems. In comparison with those of existing parameters identification schemes, the unknown parameters are identified by adaptive lag laws, and the delay time is also identified in this Letter. Numerical simulations are also given to show the effectiveness of the proposed method.