Jianfu Yang

Exponential stability and periodicity of impulsive cellular neural networks with time delays

Abstract This paper is concerned with the stability and periodicity for a class of impulsive neural networks with delays. By means of the Fixed point theory, Lyapunov functional and analysis technique, some sufficient conditions of exponential stability and periodicity are obtained. We can see that impulses do contribution to the stability and periodicity. An example is given to demonstrate the effectiveness of the obtained results.

Globally Exponential Stability of a Class of Neural Networks with Variable Delays

In this paper we study the globally exponential stability of a class of neural networks with time-varying delays. Applying idea of vector Lyapunov function, Young inequality and Halanay differential inequality with delay, the sufficient conditions for the existence and globally exponential stability of the equilibrium point are obtained

Exponential stability of a class of impulsive neural networks with variable delays

The main purpose of this paper is to study the globally exponential stability of the equilibrium point for a class of impulsive neural networks with time-varying delays. Without assuming global Lipschitz conditions on the activation functions, applying idea of vector Lyapunov function, combining Halanay differential inequality with delay, the sufficient conditions for globally exponential stability of neural networks are obtained.

A note of “global exponential stability of reaction-diffusion recurrent neural networks with time-varying delays”

Employing general Halanay inequalities, Dinipsilas derivative and functional analysis techniques, several global exponential stability criteria of the equilibrium point are established for a class of delayed reaction-diffusion recurrent neural networks. These criteria are only dependent on the parameters of the system. The results correct some errors of the earlier publication.

Global exponential stability of BAM type Cohen-Grossberg neural network with delays and impulsive on time scales

By Lyapunov function and Halanay inequality etc., a class of the BAM type Cohen-Grossberg neural network with delays and impulsive is considered on time scales. It obtained some sufficient conditions about the existence of equilibrium point, globally exponential stability and globally exponentially robust stability. We can see the impulses do contribution to the exponential stability and robust stability. At last, some applications and examples can be demonstrate the results.

Exponential stability of BAM type Cohen-Grossberg neural networks with variable delays and impulsive on time scales

In this paper, we investigate impulsive effects on the stability of BAM type Cohen-Grossberg neural networks with variable delays and obtain some sufficient conditions ensuring exponential stability of the impulsive variable delays system on time scales. The results extend and improve some recent works for impulsive neural networks as well as non-impulsive neural networks(or on time scales).

Stability of impulsive cohen-grossberg neural networks with delays

In this paper, with assuming global Lipschitz conditions on the activation functions, applying idea of vector Lyapunov function, Young inequality and Halanay differential inequality with delay, the global exponential stability of the equilibrium point for a class of Cohen-Grossberg neural networks with time-varying delays and impulses is investigated, the sufficient conditions for globally exponential stability of neural networks are obtained.

Globally exponential stability of a class of neural networks with impulses and variable delays

In this paper, a class of impulsive neural networks with time-varying delays is considered to study the globally exponential stability New sufficient conditions for globally exponential stability are obtained by using the vector Lyapunov function, Young inequality and Halanay differential inequality with delay.

Globally exponential stability analysis of neural networks with distributed delays

This paper analyses the existence and globally exponential stability of a class of cellular neural networks with distributed delays, with assuming global Lipschitz conditions on the activation functions, applying the idea of vector Lyapunov function, Young inequality and Halanay differential inequality with delay, some sufficient conditions are obtained to ensure the equilibrium point.

Criteria for exponential stability of neural networks with distributed delays

In this paper, the global exponential stability is studied for a class of cellular neural networks with distributed delays. With assuming global Lipschitz conditions on the activation functions, based on the vector Lyapunov function, using the technique by virtue of Young inequality and Halanay differential inequality with delay, some sufficient conditions are obtained to ensure the uniqueness equilibrium point and globally exponential stability.