Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on del...Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functional, is simple and valid for the stability analysis of neural networks with delay. Some previously established results in this paper are shown to be special casses of the presented result.展开更多
This paper proposes a new impulsive complex delayed dynamical network model with output coupling, which is totally different from some existing network models. Then, by employing impulsive delay differential inequalit...This paper proposes a new impulsive complex delayed dynamical network model with output coupling, which is totally different from some existing network models. Then, by employing impulsive delay differential inequalities, some sufficient conditions are obtained to guarantee the global exponential state synchronization and output synchronization of the impulsive complex delayed dynamical network. Finally, two numerical examples are given to demonstrate the effectiveness of the obtained results.展开更多
In this paper, we first introduce the concept of k-globally asymptotic stability and present a differential-difference inequality with infinite delay. By combining nonlinear inequality and nonlinear variation-of-param...In this paper, we first introduce the concept of k-globally asymptotic stability and present a differential-difference inequality with infinite delay. By combining nonlinear inequality and nonlinear variation-of-parameters formula, we derive the k-globally asymptotic stability criteria for nonlinear neutral system with infinite delay. In the end of this paper, an example is given to illustrate our theory.展开更多
文摘Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functional, is simple and valid for the stability analysis of neural networks with delay. Some previously established results in this paper are shown to be special casses of the presented result.
基金supported by Key Project of Chinese Education Ministry(No.212138)Natural Science Foundation of Chongqing(No.CQ CSTC2011BB0117)+1 种基金Foundation of Science and Technology Project of Chongqing Education Commission(No.KJ120630)Innovation Foundation of BUAA for PhD Graduates(No.YWF-12-RBYJ-005)
文摘This paper proposes a new impulsive complex delayed dynamical network model with output coupling, which is totally different from some existing network models. Then, by employing impulsive delay differential inequalities, some sufficient conditions are obtained to guarantee the global exponential state synchronization and output synchronization of the impulsive complex delayed dynamical network. Finally, two numerical examples are given to demonstrate the effectiveness of the obtained results.
基金the National Natural Science Foundation of China (10461006)the Younger Foundation of Yantai University (SX06Z9)
文摘In this paper, we first introduce the concept of k-globally asymptotic stability and present a differential-difference inequality with infinite delay. By combining nonlinear inequality and nonlinear variation-of-parameters formula, we derive the k-globally asymptotic stability criteria for nonlinear neutral system with infinite delay. In the end of this paper, an example is given to illustrate our theory.
