Impulsive BAM Neural Networks Research Articles

Exponential stability of stochastic high-order BAM neural networks with time delays and impulsive effects

In this paper, we consider the problem on exponential stability analysis of the stochastic impulsive high-order BAM neural networks with time delays. Through employing Lyapunov function method and stochastic bidirected halanay inequality, we constitute exponential stability of the stochastic impulsive high-order BAM neural networks with its estimated exponential convergence rate and feasible interval of impulsive strength. An example illustrates the main results.

Stochastic stability of impulsive BAM neural networks with time delays

The problem of stability analysis of stochastic BAM neural networks with time delays and impulse effects is investigated in this paper. Using the Lyapunov technique and the generalized Hanalay inequality, we characterize theoretically the aggregated effects of impulse and stability properties of the impulse-free version. The present approaches allow us to estimate the feasible upper bounds of impulse strengths and can also extend to the more general impulsive nonlinear systems with delays.

Mean square exponential stability in high-order stochastic impulsive BAM neural networks with time-varying delays

In this paper, a class of stochastic impulsive high-order BAM neural networks with time-varying delays is considered. By using Lyapunov functional method, LMI method and mathematics induction, some sufficient conditions are derived for the globally exponential stability of the equilibrium point of the neural networks in mean square. It is believed that these results are significant and useful for the design and applications of impulsive stochastic high-order BAM neural networks.

Global Exponential Stability for Impulsive BAM Neural Networks with Distributed Delays on Time Scales

In this paper, by utilizing the time scale calculus theory, topological degree theory and Holder's inequality on time scales, we analyze a class of impulsive BAM neural networks with distributed delays on time scales. Some sufficient conditions are obtained to ensure the existence, uniqueness and the global exponential stability of the equilibrium point. Finally, an example is provided to demonstrate the effectiveness of the results.

Global exponential stability of impulsive BAM neural networks with distributed delays

In this paper, the existence and uniqueness, and the global exponential stability (GES) of equilibrium point of a impulsive BAM neural networks with distributed delays are considered. Here we point out, different from previous methods, we do not construct Lyapunov functional or use the properties of nonsingular M-matrix. We use some basic analytical technique, such as contraction mapping principle and differential inequality technique to obtain our results. It is shown that, in some case, the stability criteria can be easily checked. Several remarks are worked out to demonstrate the advantage of our results.

Delay-dependent stability analysis for impulsive BAM neural networks with time-varying delays

In this paper, we investigate a class of impulsive BAM neural networks with time-varying delays. By establishing the delay differential inequality with impulsive initial conditions and employing M-matrix theory, we find some new sufficient conditions ensuring the existence, uniqueness and global exponential stability of the equilibrium point for impulsive BAM neural networks with time-varying delays. In particular, the estimate of the exponential convergence rate is also provided, which depends on the system parameters. An example is given to show the effectiveness of the results obtained here.

Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.