变时滞脉冲BAM神经网络的稳定性(英文)

本文研究了当脉冲不太频繁易受大的脉冲影响下的时滞BAM神经网络具有唯一的指数稳定的平衡点.分析采用一个推广的比较原理和M-矩阵理论,得到一些关于唯一平衡点的收敛性的简单可行的充分条件.所得结果推广和改进了已有文献的结果.     

时滞BAM神经网络周期解的存在性和全局指数稳定性

本文利用迭合度理论,通过构造适当的Lyapunov泛函并结合Yang不等式分析技巧,获得了具周期系数的时滞BAM神经网络周期解的存在性和全局指数稳定性的充分条件,这些结果对设计全局指数稳定的BAM神经网络与周期振荡的BAM神经网络具有重要的指导意义.     

EXISTENCE AND STABILITY OF PERIODIC SOLUTIONS OF DELAYED BAM NEURAL NETWORKS WITH PERIODIC COEFFICIENTS

In this paper, we study the BAM neural networks with variable coefficients and delays. By using the Banach fixed point theorem and constructing suitable Lyapunov function, we obtain some sufficient conditions ensuring the existence, uniqueness and global stability of periodic solution. These results are helpful to design global exponential stable BAM networks and periodic oscillatory BAM networks.     

Existence and global exponential stability of periodic solution for impulsive Cohen-Grossberg-type BAM neural networks with continuously distributed delays

This paper is concerned with the existence and global exponential stability of periodic solution for a class of impulsive Cohen-Grossberg-type BAM neural networks with continuously distributed delays. Some sufficient conditions ensuring the existence and global exponential stability of periodic solution are derived by constructing a suitable Lyapunov function and a new differential inequality. The proposed method can also be applied to study the impulsive Cohen-Grossberg-type BAM neural networks with finite distributed delays. The results in this paper extend and improve the earlier publications. Finally, two examples with numerical simulations are given to demonstrate the obtained results.     

Global asymptotic stability of BAM neural networks with mixed delays and impulses

In this paper, BAM neural networks with mixed delays and impulses are considered. A new set of sufficient conditions are derived by constructing suitable Lyapunov functional with matrix theory for the global asymptotic stability of BAM neural networks with mixed delays and impulses. Moreover, an example is also provided to illustrate the effectiveness of the results.     

带分布时滞的双向联想记忆神经网络周期解的存在性

讨论了-类带分布时滞的双向联想记忆神经网络周期解的存在性.通过运用Mawhin迭合度连续性定理,和-些不等式的分析技巧,得到了-些保证周期解存在的充分条件.     

ABOUT HYBRID BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DISCRETE DELAYS

In this paper, hybrid bidirectional associative memory neural networks with discrete delays is considered. By ingeniously importing real parameters di > 0(i = 1,2,···,n) which can be adjusted, we establish some new sufficient conditions for the dynamical characteristics of hybrid bidirectional associative memory neural networks with discrete delays by the method of variation of parameters and some analysis techniques. Our results generalize and improve the related results in [10,11]. Our work is significant...     

Almost periodic dynamical behaviors for generalized Cohen–Grossberg neural networks with discontinuous activations via differential inclusions

In this paper, we investigate the almost periodic dynamical behaviors for a class of general Cohen–Grossberg neural networks with discontinuous right-hand sides, time-varying and distributed delays. By means of retarded differential inclusions theory and nonsmooth analysis theory with generalized Lyapunov approach, we obtain the existence, uniqueness and global stability of almost periodic solution to the neural networks system. It is worthy to pointed out that, without assuming the boundedness or monotonicity of the discontinuous neuron activation functions, our results will also be valid. Finally, we give some numerical examples to show the applicability and effectiveness of our main results.     

Functional differential inclusions and dynamic behaviors for memristor-based BAM neural networks with time-varying delays

In this paper, we formulate and investigate a class of memristor-based BAM neural networks with time-varying delays. Under the framework of Filippov solutions, the viability and dissipativity of solutions for functional differential inclusions and memristive BAM neural networks can be guaranteed by the matrix measure approach and generalized Halanay inequalities. Then, a new method involving the application of set-valued version of Krasnoselskii’ fixed point theorem in a cone is successfully employed to derive the existence of the positive periodic solution. The dynamic analysis in this paper utilizes the theory of set-valued maps and functional differential equations with discontinuous right-hand sides of Filippov type. The obtained results extend and improve some previous works on conventional BAM neural networks. Finally, numerical examples are given to demonstrate the theoretical results via computer simulations.     

Existence and globally exponential stability of almost periodic solution for Cohen–Grossberg BAM neural networks with variable coefficients

In this paper, a class of Cohen–Grossberg BAM neural networks with variable coefficients are studied. Some sufficient conditions are established for the existence and uniqueness of the almost periodic solution. These results have important leading significance in designs and applications of Cohen–Grossberg BAM neural networks. Finally, an example is presented to illustrate the feasibility and effectiveness of the results.     

变时滞BAM神经网络的全局指数稳定性

研究一类变时滞BAM神经网络平衡点的全局指数稳定性问题.在不要求激励函数全局Lipschitz条件下,通过构造合适的Lyapunov泛函,并结合Young不等式,得到了BAM神经网络模型在一定条件下全局指数稳定的一些充分条件,推广和改进了前人的相关结论,为综合设计指数稳定的时滞BAM神经网络提供了依据.     

PERIODIC SOLUTIONS OF BAM NETWORKS WITH STATE DEPENDENT DELAYS

We use Krasnosel'skii's fixed point theorem to show that the following BAM networks with state dependent delays has a periodic solution.     

时滞细胞神经网络概周期解的存在性与全局指数稳定性

通过利用Banach 空间中的不动点定理和Lyapunov泛函的方法,获得了时滞细胞神经网络概周期解的存在性与全局指数稳定性的新结论. 所获结果在较大程度上改进和推广了已有文献中的相应结果.     

THE EXISTENCE OF ALMOST PERIODIC SOLUTION FOR CELLULAR NEURAL NETWORKS WITH VARIABLE COEFFICIENTS AND DELAYS

In this paper, we study the problem of almost periodic solution for the cellular neural networks with variable coefficients and delays. By ingeniously importing real parameters di > 0(i = 1,2,…n) which can be adjusted, we establish some sufficient conditions which ensure the neural networks exist a unique almost periodic solution, and all other solutions exponentially converge to such an almost periodic solution.     

具时滞的Hopfield网络周期解的指数稳定性

The paper is devoted to periodic attractor of delayed Hopfield neural networks with time-varying.By constructing Lyapunov functionals and using inequality techniques,some new sufficient criteria are obtained to guarantee the existence and global exponential stability of periodic attractor.Our results improve and extend some existing ones in[13～14].One example is also worked out to demonstrate the advantages of our results.